Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2543-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-2543-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 May 2021
Research article |
| 18 May 2021
| 18 May 2021
Resampling and ensemble techniques for improving ANN-based high-flow forecast accuracy
Everett Snieder
Department of Civil Engineering, York University, 4700 Keele St, Toronto ON, M3J 1P3, Canada
Karen Abogadil
Department of Civil Engineering, York University, 4700 Keele St, Toronto ON, M3J 1P3, Canada
Usman T. Khan
CORRESPONDING AUTHOR
Department of Civil Engineering, York University, 4700 Keele St, Toronto ON, M3J 1P3, Canada
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Everett Snieder and Usman T. Khan
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-169, https://doi.org/10.5194/hess-2024-169, 2024
Revised manuscript under review for HESS
Short summary
Short summary
Improving the accuracy of flood forecasts is paramount to minimising flood damage. Machine-learning models are increasingly being applied for flood forecasting. Such models are typically trained to large historic hydrometeorological datasets. In this work, we evaluate methods for selecting training datasets, that maximise the spatiotemproal diversity of the represented hydrological processes. Empirical results showcase the importance of hydrological diversity in training ML models.
Everett Snieder and Usman T. Khan
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-169, https://doi.org/10.5194/hess-2024-169, 2024
Revised manuscript under review for HESS
Short summary
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Improving the accuracy of flood forecasts is paramount to minimising flood damage. Machine-learning models are increasingly being applied for flood forecasting. Such models are typically trained to large historic hydrometeorological datasets. In this work, we evaluate methods for selecting training datasets, that maximise the spatiotemproal diversity of the represented hydrological processes. Empirical results showcase the importance of hydrological diversity in training ML models.
Usman T. Khan and Caterina Valeo
Hydrol. Earth Syst. Sci., 20, 2267–2293, https://doi.org/10.5194/hess-20-2267-2016, https://doi.org/10.5194/hess-20-2267-2016, 2016
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This paper contains a new two-step method to construct fuzzy numbers using observational data. In addition an existing fuzzy neural network is modified to account for fuzzy number inputs. This is combined with possibility-theory based intervals to train the network. Furthermore, model output and a defuzzification technique is used to estimate the risk of low Dissolved Oxygen so that water resource managers can implement strategies to prevent the occurrence of low Dissolved Oxygen.
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Subject: Urban Hydrology | Techniques and Approaches: Modelling approaches
Simulation of spatially distributed sources, transport, and transformation of nitrogen from fertilization and septic systems in a suburban watershed
Combining statistical and hydrodynamic models to assess compound flood hazards from rainfall and storm surge: a case study of Shanghai
Exploring the driving factors of compound flood severity in coastal cities: a comprehensive analytical approach
An optimized long short-term memory (LSTM)-based approach applied to early warning and forecasting of ponding in the urban drainage system
A deep-learning-technique-based data-driven model for accurate and rapid flood predictions in temporal and spatial dimensions
Impact of urban geology on model simulations of shallow groundwater levels and flow paths
Technical note: Modeling spatial fields of extreme precipitation – a hierarchical Bayesian approach
Intersecting near-real time fluvial and pluvial inundation estimates with sociodemographic vulnerability to quantify a household flood impact index
Forecasting green roof detention performance by temporal downscaling of precipitation time-series projections
Evaluating different machine learning methods to simulate runoff from extensive green roofs
Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods
The impact of the spatiotemporal structure of rainfall on flood frequency over a small urban watershed: an approach coupling stochastic storm transposition and hydrologic modeling
Space variability impacts on hydrological responses of nature-based solutions and the resulting uncertainty: a case study of Guyancourt (France)
Urban surface water flood modelling – a comprehensive review of current models and future challenges
Event selection and two-stage approach for calibrating models of green urban drainage systems
Modeling the high-resolution dynamic exposure to flooding in a city region
Drainage area characterization for evaluating green infrastructure using the Storm Water Management Model
Critical scales to explain urban hydrological response: an application in Cranbrook, London
Increase in flood risk resulting from climate change in a developed urban watershed – the role of storm temporal patterns
Patterns and comparisons of human-induced changes in river flood impacts in cities
Scale effect challenges in urban hydrology highlighted with a distributed hydrological model
Comparison of the impacts of urban development and climate change on exposing European cities to pluvial flooding
Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review
Hydrodynamics of pedestrians' instability in floodwaters
Formulating and testing a method for perturbing precipitation time series to reflect anticipated climatic changes
Using rainfall thresholds and ensemble precipitation forecasts to issue and improve urban inundation alerts
Enhancing the T-shaped learning profile when teaching hydrology using data, modeling, and visualization activities
On the sensitivity of urban hydrodynamic modelling to rainfall spatial and temporal resolution
Precipitation variability within an urban monitoring network via microcanonical cascade generators
Estimation of peak discharges of historical floods
Indirect downscaling of hourly precipitation based on atmospheric circulation and temperature
Assessing the hydrologic restoration of an urbanized area via an integrated distributed hydrological model
Using the Storm Water Management Model to predict urban headwater stream hydrological response to climate and land cover change
Evaluating scale and roughness effects in urban flood modelling using terrestrial LIDAR data
Contribution of directly connected and isolated impervious areas to urban drainage network hydrographs
Thermal management of an unconsolidated shallow urban groundwater body
Online multistep-ahead inundation depth forecasts by recurrent NARX networks
A statistical analysis of insurance damage claims related to rainfall extremes
Joint impact of rainfall and tidal level on flood risk in a coastal city with a complex river network: a case study of Fuzhou City, China
Urbanization and climate change impacts on future urban flooding in Can Tho city, Vietnam
Multi-objective optimization for combined quality–quantity urban runoff control
Development of flood probability charts for urban drainage network in coastal areas through a simplified joint assessment approach
Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks
Coupling urban event-based and catchment continuous modelling for combined sewer overflow river impact assessment
Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites
Ruoyu Zhang, Lawrence E. Band, Peter M. Groffman, Laurence Lin, Amanda K. Suchy, Jonathan M. Duncan, and Arthur J. Gold
Hydrol. Earth Syst. Sci., 28, 4599–4621, https://doi.org/10.5194/hess-28-4599-2024, https://doi.org/10.5194/hess-28-4599-2024, 2024
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Human-induced nitrogen (N) from fertilization and septic effluents is the primary N source in urban watersheds. We developed a model to understand how spatial and temporal patterns of these loads affect hydrologic and biogeochemical processes at the hillslope level. The comparable simulations to observations showed the ability of our model to enhance insights into current water quality conditions, identify high-N-retention locations, and plan future restorations to improve urban water quality.
Hanqing Xu, Elisa Ragno, Sebastiaan N. Jonkman, Jun Wang, Jeremy D. Bricker, Zhan Tian, and Laixiang Sun
Hydrol. Earth Syst. Sci., 28, 3919–3930, https://doi.org/10.5194/hess-28-3919-2024, https://doi.org/10.5194/hess-28-3919-2024, 2024
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A coupled statistical–hydrodynamic model framework is employed to quantitatively evaluate the sensitivity of compound flood hazards to the relative timing of peak storm surges and rainfall. The findings reveal that the timing difference between these two factors significantly affects flood inundation depth and extent. The most severe inundation occurs when rainfall precedes the storm surge peak by 2 h.
Yan Liu, Ting Zhang, Yi Ding, Aiqing Kang, Xiaohui Lei, and Jianzhu Li
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-100, https://doi.org/10.5194/hess-2024-100, 2024
Revised manuscript under review for HESS
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In coastal cities, rainfall and storm surges cause compound flooding. This study quantifies the contributions of rainfall and tides to compound flooding and analyzes interactions between different flood types. Findings show rainfall has a greater effect on flooding compared to tidal levels. The interaction between fluvial and pluvial flooding exacerbates the flood disaster. Notably, tidal levels have the most significant impact during the interaction phase of these flood types.
Wen Zhu, Tao Tao, Hexiang Yan, Jieru Yan, Jiaying Wang, Shuping Li, and Kunlun Xin
Hydrol. Earth Syst. Sci., 27, 2035–2050, https://doi.org/10.5194/hess-27-2035-2023, https://doi.org/10.5194/hess-27-2035-2023, 2023
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To provide a possibility for early warning and forecasting of ponding in the urban drainage system, an optimized long short-term memory (LSTM)-based model is proposed in this paper. It has a remarkable improvement compared to the models based on LSTM and convolutional neural network (CNN) structures. The performance of the corrected model is reliable if the number of monitoring sites is over one per hectare. Increasing the number of monitoring points further has little impact on the performance.
Qianqian Zhou, Shuai Teng, Zuxiang Situ, Xiaoting Liao, Junman Feng, Gongfa Chen, Jianliang Zhang, and Zonglei Lu
Hydrol. Earth Syst. Sci., 27, 1791–1808, https://doi.org/10.5194/hess-27-1791-2023, https://doi.org/10.5194/hess-27-1791-2023, 2023
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A deep-learning-based data-driven model for flood predictions in temporal and spatial dimensions, with the integration of a long short-term memory network, Bayesian optimization, and transfer learning is proposed. The model accurately predicts water depths and flood time series/dynamics for hyetograph inputs, with substantial improvements in computational time. With transfer learning, the model was well applied to a new case study and showed robust compatibility and generalization ability.
Ane LaBianca, Mette H. Mortensen, Peter Sandersen, Torben O. Sonnenborg, Karsten H. Jensen, and Jacob Kidmose
Hydrol. Earth Syst. Sci., 27, 1645–1666, https://doi.org/10.5194/hess-27-1645-2023, https://doi.org/10.5194/hess-27-1645-2023, 2023
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The study explores the effect of Anthropocene geology and the computational grid size on the simulation of shallow urban groundwater. Many cities are facing challenges with high groundwater levels close to the surface, yet urban planning and development seldom consider its impact on the groundwater resource. This study illustrates that the urban subsurface infrastructure significantly affects the groundwater flow paths and the residence time of shallow urban groundwater.
Bianca Rahill-Marier, Naresh Devineni, and Upmanu Lall
Hydrol. Earth Syst. Sci., 26, 5685–5695, https://doi.org/10.5194/hess-26-5685-2022, https://doi.org/10.5194/hess-26-5685-2022, 2022
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We present a new approach to modeling extreme regional rainfall by considering the spatial structure of extreme events. The developed models allow a probabilistic exploration of how the regional drainage network may respond to extreme rainfall events and provide a foundation for how future risks may be better estimated.
Matthew Preisser, Paola Passalacqua, R. Patrick Bixler, and Julian Hofmann
Hydrol. Earth Syst. Sci., 26, 3941–3964, https://doi.org/10.5194/hess-26-3941-2022, https://doi.org/10.5194/hess-26-3941-2022, 2022
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There is rising concern in numerous fields regarding the inequitable distribution of human risk to floods. The co-occurrence of river and surface flooding is largely excluded from leading flood hazard mapping services, therefore underestimating hazards. Using high-resolution elevation data and a region-specific social vulnerability index, we developed a method to estimate flood impacts at the household level in near-real time.
Vincent Pons, Rasmus Benestad, Edvard Sivertsen, Tone Merete Muthanna, and Jean-Luc Bertrand-Krajewski
Hydrol. Earth Syst. Sci., 26, 2855–2874, https://doi.org/10.5194/hess-26-2855-2022, https://doi.org/10.5194/hess-26-2855-2022, 2022
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Different models were developed to increase the temporal resolution of precipitation time series to minutes. Their applicability under climate change and their suitability for producing input time series for green infrastructure (e.g. green roofs) modelling were evaluated. The robustness of the model was validated against a range of European climates in eight locations in France and Norway. The future hydrological performances of green roofs were evaluated in order to improve design practice.
Elhadi Mohsen Hassan Abdalla, Vincent Pons, Virginia Stovin, Simon De-Ville, Elizabeth Fassman-Beck, Knut Alfredsen, and Tone Merete Muthanna
Hydrol. Earth Syst. Sci., 25, 5917–5935, https://doi.org/10.5194/hess-25-5917-2021, https://doi.org/10.5194/hess-25-5917-2021, 2021
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This study investigated the potential of using machine learning algorithms as hydrological models of green roofs across different climatic condition. The study provides comparison between conceptual and machine learning algorithms. Machine learning models were found to be accurate in simulating runoff from extensive green roofs.
Yang Yang and Ting Fong May Chui
Hydrol. Earth Syst. Sci., 25, 5839–5858, https://doi.org/10.5194/hess-25-5839-2021, https://doi.org/10.5194/hess-25-5839-2021, 2021
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This study uses explainable machine learning methods to model and interpret the statistical correlations between rainfall and the discharge of urban catchments with sustainable urban drainage systems. The resulting models have good prediction accuracies. However, the right predictions may be made for the wrong reasons as the model cannot provide physically plausible explanations as to why a prediction is made.
Zhengzheng Zhou, James A. Smith, Mary Lynn Baeck, Daniel B. Wright, Brianne K. Smith, and Shuguang Liu
Hydrol. Earth Syst. Sci., 25, 4701–4717, https://doi.org/10.5194/hess-25-4701-2021, https://doi.org/10.5194/hess-25-4701-2021, 2021
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The role of rainfall space–time structure in flood response is an important research issue in urban hydrology. This study contributes to this understanding in small urban watersheds. Combining stochastically based rainfall scenarios with a hydrological model, the results show the complexities of flood response for various return periods, implying the common assumptions of spatially uniform rainfall in urban flood frequency are problematic, even for relatively small basin scales.
Yangzi Qiu, Igor da Silva Rocha Paz, Feihu Chen, Pierre-Antoine Versini, Daniel Schertzer, and Ioulia Tchiguirinskaia
Hydrol. Earth Syst. Sci., 25, 3137–3162, https://doi.org/10.5194/hess-25-3137-2021, https://doi.org/10.5194/hess-25-3137-2021, 2021
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Our original research objective is to investigate the uncertainties of the hydrological responses of nature-based solutions (NBSs) that result from the multiscale space variability in both the rainfall and the NBS distribution. Results show that the intersection effects of spatial variability in rainfall and the spatial arrangement of NBS can generate uncertainties of peak flow and total runoff volume estimations in NBS scenarios.
Kaihua Guo, Mingfu Guan, and Dapeng Yu
Hydrol. Earth Syst. Sci., 25, 2843–2860, https://doi.org/10.5194/hess-25-2843-2021, https://doi.org/10.5194/hess-25-2843-2021, 2021
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This study presents a comprehensive review of models and emerging approaches for predicting urban surface water flooding driven by intense rainfall. It explores the advantages and limitations of existing models and identifies major challenges. Issues of model complexities, scale effects, and computational efficiency are also analysed. The results will inform scientists, engineers, and decision-makers of the latest developments and guide the model selection based on desired objectives.
Ico Broekhuizen, Günther Leonhardt, Jiri Marsalek, and Maria Viklander
Hydrol. Earth Syst. Sci., 24, 869–885, https://doi.org/10.5194/hess-24-869-2020, https://doi.org/10.5194/hess-24-869-2020, 2020
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Urban drainage models are usually calibrated using a few events so that they accurately represent a real-world site. This paper compares 14 single- and two-stage strategies for selecting these events and found significant variation between them in terms of model performance and the obtained values of model parameters. Calibrating parameters for green and impermeable areas in two separate stages improved model performance in the validation period while making calibration easier and faster.
Xuehong Zhu, Qiang Dai, Dawei Han, Lu Zhuo, Shaonan Zhu, and Shuliang Zhang
Hydrol. Earth Syst. Sci., 23, 3353–3372, https://doi.org/10.5194/hess-23-3353-2019, https://doi.org/10.5194/hess-23-3353-2019, 2019
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Urban flooding exposure is generally investigated with the assumption of stationary disasters and disaster-hit bodies during an event, and thus it cannot satisfy the increasingly elaborate modeling and management of urban floods. In this study, a comprehensive method was proposed to simulate dynamic exposure to urban flooding considering human mobility. Several scenarios, including diverse flooding types and various responses of residents to flooding, were considered.
Joong Gwang Lee, Christopher T. Nietch, and Srinivas Panguluri
Hydrol. Earth Syst. Sci., 22, 2615–2635, https://doi.org/10.5194/hess-22-2615-2018, https://doi.org/10.5194/hess-22-2615-2018, 2018
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This paper demonstrates an approach to spatial discretization for analyzing green infrastructure (GI) using SWMM. Besides DCIA, pervious buffers should be identified for GI modeling. Runoff contributions from different spatial components and flow pathways would impact GI performance. The presented approach can reduce the number of calibration parameters and apply scale–independently to a watershed scale. Hydrograph separation can add insights for developing GI scenarios.
Elena Cristiano, Marie-Claire ten Veldhuis, Santiago Gaitan, Susana Ochoa Rodriguez, and Nick van de Giesen
Hydrol. Earth Syst. Sci., 22, 2425–2447, https://doi.org/10.5194/hess-22-2425-2018, https://doi.org/10.5194/hess-22-2425-2018, 2018
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In this work we investigate the influence rainfall and catchment scales have on hydrological response. This problem is quite relevant in urban areas, where the response is fast due to the high degree of imperviousness. We presented a new approach to classify rainfall variability in space and time and use this classification to investigate rainfall aggregation effects on urban hydrological response. This classification allows the spatial extension of the main core of the storm to be identified.
Suresh Hettiarachchi, Conrad Wasko, and Ashish Sharma
Hydrol. Earth Syst. Sci., 22, 2041–2056, https://doi.org/10.5194/hess-22-2041-2018, https://doi.org/10.5194/hess-22-2041-2018, 2018
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The study examines the impact of higher temperatures expected in a future climate on how rainfall varies with time during severe storm events. The results show that these impacts increase future flood risk in urban environments and that current design guidelines need to be adjusted so that effective adaptation measures can be implemented.
Stephanie Clark, Ashish Sharma, and Scott A. Sisson
Hydrol. Earth Syst. Sci., 22, 1793–1810, https://doi.org/10.5194/hess-22-1793-2018, https://doi.org/10.5194/hess-22-1793-2018, 2018
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This study investigates global patterns relating urban river flood impacts to socioeconomic development and changing hydrologic conditions, and comparisons are provided between 98 individual cities. This paper condenses and communicates large amounts of information to accelerate the understanding of relationships between local urban conditions and global processes, and to potentially motivate knowledge transfer between decision-makers facing similar circumstances.
Abdellah Ichiba, Auguste Gires, Ioulia Tchiguirinskaia, Daniel Schertzer, Philippe Bompard, and Marie-Claire Ten Veldhuis
Hydrol. Earth Syst. Sci., 22, 331–350, https://doi.org/10.5194/hess-22-331-2018, https://doi.org/10.5194/hess-22-331-2018, 2018
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This paper proposes a two-step investigation to illustrate the extent of scale effects in urban hydrology. First, fractal tools are used to highlight the scale dependency observed within GIS data inputted in urban hydrological models. Then an intensive multi-scale modelling work was carried out to confirm effects on model performances. The model was implemented at 17 spatial resolutions ranging from 100 to 5 m. Results allow the understanding of scale challenges in hydrology modelling.
Per Skougaard Kaspersen, Nanna Høegh Ravn, Karsten Arnbjerg-Nielsen, Henrik Madsen, and Martin Drews
Hydrol. Earth Syst. Sci., 21, 4131–4147, https://doi.org/10.5194/hess-21-4131-2017, https://doi.org/10.5194/hess-21-4131-2017, 2017
Elena Cristiano, Marie-Claire ten Veldhuis, and Nick van de Giesen
Hydrol. Earth Syst. Sci., 21, 3859–3878, https://doi.org/10.5194/hess-21-3859-2017, https://doi.org/10.5194/hess-21-3859-2017, 2017
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In the last decades, new instruments were developed to measure rainfall and hydrological processes at high resolution. Weather radars are used, for example, to measure how rainfall varies in space and time. At the same time, new models were proposed to reproduce and predict hydrological response, in order to prevent flooding in urban areas. This paper presents a review of our current knowledge of rainfall and hydrological processes in urban areas, focusing on their variability in time and space.
Chiara Arrighi, Hocine Oumeraci, and Fabio Castelli
Hydrol. Earth Syst. Sci., 21, 515–531, https://doi.org/10.5194/hess-21-515-2017, https://doi.org/10.5194/hess-21-515-2017, 2017
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In developed countries, the majority of fatalities during floods occurs as a consequence of inappropriate high-risk behaviour such as walking or driving in floodwaters. This work addresses pedestrians' instability in floodwaters. It analyses both the contribution of flood and human physical characteristics in the loss of stability highlighting the key role of subject height (submergence) and flow regime. The method consists of a re-analysis of experiments and numerical modelling.
Hjalte Jomo Danielsen Sørup, Stylianos Georgiadis, Ida Bülow Gregersen, and Karsten Arnbjerg-Nielsen
Hydrol. Earth Syst. Sci., 21, 345–355, https://doi.org/10.5194/hess-21-345-2017, https://doi.org/10.5194/hess-21-345-2017, 2017
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In this study we propose a methodology changing present-day precipitation time series to reflect future changed climate. Present-day time series have a much finer resolution than what is provided by climate models and thus have a much broader application range. The proposed methodology is able to replicate most expectations of climate change precipitation. These time series can be used to run fine-scale hydrological and hydraulic models and thereby assess the influence of climate change on them.
Tsun-Hua Yang, Gong-Do Hwang, Chin-Cheng Tsai, and Jui-Yi Ho
Hydrol. Earth Syst. Sci., 20, 4731–4745, https://doi.org/10.5194/hess-20-4731-2016, https://doi.org/10.5194/hess-20-4731-2016, 2016
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Taiwan continues to suffer from floods. This study proposes the integration of rainfall thresholds and ensemble precipitation forecasts to provide probabilistic urban inundation forecasts. Utilization of ensemble precipitation forecasts can extend forecast lead times to 72 h, preceding peak flows and allowing response agencies to take necessary preparatory measures. This study also develops a hybrid of real-time observation and rainfall forecasts to improve the first 24 h inundation forecasts.
Christopher A. Sanchez, Benjamin L. Ruddell, Roy Schiesser, and Venkatesh Merwade
Hydrol. Earth Syst. Sci., 20, 1289–1299, https://doi.org/10.5194/hess-20-1289-2016, https://doi.org/10.5194/hess-20-1289-2016, 2016
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The use of authentic learning activities is especially important for place-based geosciences like hydrology, where professional breadth and technical depth are critical for practicing hydrologists. The current study found that integrating computerized learning content into the learning experience, using only a simple spreadsheet tool and readily available hydrological data, can effectively bring the "real world" into the classroom and provide an enriching educational experience.
G. Bruni, R. Reinoso, N. C. van de Giesen, F. H. L. R. Clemens, and J. A. E. ten Veldhuis
Hydrol. Earth Syst. Sci., 19, 691–709, https://doi.org/10.5194/hess-19-691-2015, https://doi.org/10.5194/hess-19-691-2015, 2015
P. Licznar, C. De Michele, and W. Adamowski
Hydrol. Earth Syst. Sci., 19, 485–506, https://doi.org/10.5194/hess-19-485-2015, https://doi.org/10.5194/hess-19-485-2015, 2015
J. Herget, T. Roggenkamp, and M. Krell
Hydrol. Earth Syst. Sci., 18, 4029–4037, https://doi.org/10.5194/hess-18-4029-2014, https://doi.org/10.5194/hess-18-4029-2014, 2014
F. Beck and A. Bárdossy
Hydrol. Earth Syst. Sci., 17, 4851–4863, https://doi.org/10.5194/hess-17-4851-2013, https://doi.org/10.5194/hess-17-4851-2013, 2013
D. H. Trinh and T. F. M. Chui
Hydrol. Earth Syst. Sci., 17, 4789–4801, https://doi.org/10.5194/hess-17-4789-2013, https://doi.org/10.5194/hess-17-4789-2013, 2013
J. Y. Wu, J. R. Thompson, R. K. Kolka, K. J. Franz, and T. W. Stewart
Hydrol. Earth Syst. Sci., 17, 4743–4758, https://doi.org/10.5194/hess-17-4743-2013, https://doi.org/10.5194/hess-17-4743-2013, 2013
H. Ozdemir, C. C. Sampson, G. A. M. de Almeida, and P. D. Bates
Hydrol. Earth Syst. Sci., 17, 4015–4030, https://doi.org/10.5194/hess-17-4015-2013, https://doi.org/10.5194/hess-17-4015-2013, 2013
Y. Seo, N.-J. Choi, and A. R. Schmidt
Hydrol. Earth Syst. Sci., 17, 3473–3483, https://doi.org/10.5194/hess-17-3473-2013, https://doi.org/10.5194/hess-17-3473-2013, 2013
J. Epting, F. Händel, and P. Huggenberger
Hydrol. Earth Syst. Sci., 17, 1851–1869, https://doi.org/10.5194/hess-17-1851-2013, https://doi.org/10.5194/hess-17-1851-2013, 2013
H.-Y. Shen and L.-C. Chang
Hydrol. Earth Syst. Sci., 17, 935–945, https://doi.org/10.5194/hess-17-935-2013, https://doi.org/10.5194/hess-17-935-2013, 2013
M. H. Spekkers, M. Kok, F. H. L. R. Clemens, and J. A. E. ten Veldhuis
Hydrol. Earth Syst. Sci., 17, 913–922, https://doi.org/10.5194/hess-17-913-2013, https://doi.org/10.5194/hess-17-913-2013, 2013
J. J. Lian, K. Xu, and C. Ma
Hydrol. Earth Syst. Sci., 17, 679–689, https://doi.org/10.5194/hess-17-679-2013, https://doi.org/10.5194/hess-17-679-2013, 2013
H. T. L. Huong and A. Pathirana
Hydrol. Earth Syst. Sci., 17, 379–394, https://doi.org/10.5194/hess-17-379-2013, https://doi.org/10.5194/hess-17-379-2013, 2013
S. Oraei Zare, B. Saghafian, and A. Shamsai
Hydrol. Earth Syst. Sci., 16, 4531–4542, https://doi.org/10.5194/hess-16-4531-2012, https://doi.org/10.5194/hess-16-4531-2012, 2012
R. Archetti, A. Bolognesi, A. Casadio, and M. Maglionico
Hydrol. Earth Syst. Sci., 15, 3115–3122, https://doi.org/10.5194/hess-15-3115-2011, https://doi.org/10.5194/hess-15-3115-2011, 2011
Y.-M. Chiang, L.-C. Chang, M.-J. Tsai, Y.-F. Wang, and F.-J. Chang
Hydrol. Earth Syst. Sci., 15, 185–196, https://doi.org/10.5194/hess-15-185-2011, https://doi.org/10.5194/hess-15-185-2011, 2011
I. Andrés-Doménech, J. C. Múnera, F. Francés, and J. B. Marco
Hydrol. Earth Syst. Sci., 14, 2057–2072, https://doi.org/10.5194/hess-14-2057-2010, https://doi.org/10.5194/hess-14-2057-2010, 2010
Yen-Ming Chiang, Li-Chiu Chang, Meng-Jung Tsai, Yi-Fung Wang, and Fi-John Chang
Hydrol. Earth Syst. Sci., 14, 1309–1319, https://doi.org/10.5194/hess-14-1309-2010, https://doi.org/10.5194/hess-14-1309-2010, 2010
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Abbot, J. and Marohasy, J.: Input selection and optimisation for monthly
rainfall forecasting in Queensland, Australia, using artificial neural
networks, Atmos. Res., 138, 166–178,
https://doi.org/10.1016/j.atmosres.2013.11.002, 2014. a
Abrahart, R. J., Heppenstall, A. J., and See, L. M.: Timing error correction
procedure applied to neural network rainfall-runoff modelling, Hydrolog.
Sci. J., 52, 414–431, https://doi.org/10.1623/hysj.52.3.414, 2007. a, b, c, d
Abrahart, R. J., Anctil, F., Coulibaly, P., Dawson, C. W., Mount, N. J., See,
L. M., Shamseldin, A. Y., Solomatine, D. P., Toth, E., and Wilby, R. L.: Two
decades of anarchy? Emerging themes and outstanding challenges for neural
network river forecasting, Prog. Phys. Geog., 36, 480–513, https://doi.org/10.1177/0309133312444943, 2012. a, b, c
Anctil, F. and Lauzon, N.: Generalisation for neural networks through data sampling and training procedures, with applications to streamflow predictions, Hydrol. Earth Syst. Sci., 8, 940–958, https://doi.org/10.5194/hess-8-940-2004, 2004. a, b, c
Atieh, M., Taylor, G., Sattar, A. M. A., and Gharabaghi, B.: Prediction of flow
duration curves for ungauged basins, J. Hydrol., 545, 383–394,
https://doi.org/10.1016/j.jhydrol.2016.12.048, 2017. a
Banjac, G., Vašak, M., and Baotić, M.: Adaptable urban water
demand prediction system, Water Supply, 15, 958–964,
https://doi.org/10.2166/ws.2015.048, 2015. a
Barzegar, R., Ghasri, M., Qi, Z., Quilty, J., and Adamowski, J.: Using
bootstrap ELM and LSSVM models to estimate river ice thickness in the
Mackenzie River Basin in the Northwest Territories, Canada, J.
Hydrol., 577, 123903, https://doi.org/10.1016/j.jhydrol.2019.06.075, 2019. a
Bennett, N. D., Croke, B. F., Guariso, G., Guillaume, J. H., Hamilton, S. H.,
Jakeman, A. J., Marsili-Libelli, S., Newham, L. T., Norton, J. P., Perrin,
C., Pierce, S. A., Robson, B., Seppelt, R., Voinov, A. A., Fath, B. D., and
Andreassian, V.: Characterising performance of environmental models,
Environ. Modell. Softw., 40, 1–20,
https://doi.org/10.1016/j.envsoft.2012.09.011, 2013. a
Błaszczyński, J. and Stefanowski, J.: Neighbourhood sampling in
bagging for imbalanced data, Neurocomputing, 150, 529–542,
https://doi.org/10.1016/j.neucom.2014.07.064, 2015. a
Breiman, L.: Bagging predictors, Mach. Learn., 24, 123–140,
https://doi.org/10.1007/BF00058655, 1996. a, b, c
Cannon, A. J. and Whitfield, P. H.: Downscaling recent streamflow conditions
in British Columbia, Canada using ensemble neural network models, J.
Hydrol., 259, 136–151, https://doi.org/10.1016/S0022-1694(01)00581-9, 2002. a
Chapi, K., Singh, V. P., Shirzadi, A., Shahabi, H., Bui, D. T., Pham, B. T.,
and Khosravi, K.: A novel hybrid artificial intelligence approach for flood
susceptibility assessment, Environ. Modell. Softw., 95,
229–245, https://doi.org/10.1016/j.envsoft.2017.06.012, 2017. a
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P.: SMOTE:
Synthetic minority over-sampling technique, J. Artif.
Intell. Res., 16, 321–357, https://doi.org/10.1613/jair.953, 2002. a
Chen, W., Hong, H., Li, S., Shahabi, H., Wang, Y., Wang, X., and Ahmad, B. B.:
Flood susceptibility modelling using novel hybrid approach of reduced-error
pruning trees with bagging and random subspace ensembles, J.
Hydrol., 575, 864–873, https://doi.org/10.1016/j.jhydrol.2019.05.089, 2019. a
Crochemore, L., Perrin, C., Andréassian, V., Ehret, U., Seibert, S. P.,
Grimaldi, S., Gupta, H., and Paturel, J.-E.: Comparing expert judgement and
numerical criteria for hydrograph evaluation, Hydrolog. Sci. J.,
60, 402–423, https://doi.org/10.1080/02626667.2014.903331, 2015. a, b, c
Dawson, C. W. and Wilby, R. L.: Hydrological modelling using artificial neural
networks, Prog. Phys. Geogr., 25,
80–108, https://doi.org/10.1177/030913330102500104, 2001. a
de Vos, N. and Rientjes, T.: Correction of Timing Errors of Artificial Neural
Network Rainfall-Runoff Models, in: Practical Hydroinformatics, pp.
101–112, Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-540-79881-1_8,
2009. a, b, c, d
de Vos, N. J. and Rientjes, T. H. M.: Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation, Hydrol. Earth Syst. Sci., 9, 111–126, https://doi.org/10.5194/hess-9-111-2005, 2005. a
Díez-Pastor, J. F., Rodríguez, J. J., García-Osorio, C., and
Kuncheva, L. I.: Random Balance: Ensembles of variable priors classifiers
for imbalanced data, Knowledge-Based Syst., 85, 96–111,
https://doi.org/10.1016/j.knosys.2015.04.022, 2015a. a, b
Díez-Pastor, J. F., Rodríguez, J. J., García-Osorio, C. I.,
and Kuncheva, L. I.: Diversity techniques improve the performance of the
best imbalance learning ensembles, Inform. Sci., 325, 98–117,
https://doi.org/10.1016/j.ins.2015.07.025, 2015b. a
Duncan, A.: The analysis and application of Artificial Neural Networks for early warning systems in hydrology and the environment, PhD thesis, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK, 2014. a
Ehret, U. and Zehe, E.: Series distance – an intuitive metric to quantify hydrograph similarity in terms of occurrence, amplitude and timing of hydrological events, Hydrol. Earth Syst. Sci., 15, 877–896, https://doi.org/10.5194/hess-15-877-2011, 2011. a, b, c
Erdal, H. I. and Karakurt, O.: Advancing monthly streamflow prediction
accuracy of CART models using ensemble learning paradigms, J.
Hydrol., 477, 119–128, https://doi.org/10.1016/j.jhydrol.2012.11.015, 2013. a
Fernando, T., Maier, H., and Dandy, G.: Selection of input variables for data
driven models: An average shifted histogram partial mutual information
estimator approach, J. Hydrol., 367, 165–176,
https://doi.org/10.1016/j.jhydrol.2008.10.019, 2009. a
Fleming, S. W., Bourdin, D. R., Campbell, D., Stull, R. B., and Gardner, T.:
Development and operational testing of a super-ensemble artificial
intelligence flood-forecast model for a pacific northwest river, J.
Am. Water Resour. As., 51, 502–512,
https://doi.org/10.1111/jawr.12259, 2015. a, b, c, d
Freund, Y. and Schapire, R. E.: Experiments with a New Boosting Algorithm, in: ICML'96: Proceedings of the 13th International Conference on Machine Learning, Bari, Italy, 3–6 July 1996, 148–156, 1996. a
Galar, M., Fernández, A., Barrenechea, E., and Herrera, F.: EUSBoost:
Enhancing ensembles for highly imbalanced data-sets by evolutionary
undersampling, Pattern Recognition, 46, 3460–3471,
https://doi.org/10.1016/j.patcog.2013.05.006, 2013. a
Govindaraju, R. S.: Artificial Neural Networks in Hydrology. II: Hydrologic
Applications, J. Hydrol. Eng., 5, 124–137,
https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(124), 2000. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., and Bing, G.:
Learning from class-imbalanced data: Review of methods and applications, Expert Syst. Appl., 73, 220–239,
https://doi.org/10.1016/j.eswa.2016.12.035, 2017. a, b, c
Hastie, T., Tibshirani, R., and Friedman, J.: Elements of Statistical Learning,
2nd ed., no. 2 in Springer Series in Statistics, Springer New York, New
York, NY, https://doi.org/10.1007/978-0-387-84858-7, 2009. a
He, J., Valeo, C., Chu, A., and Neumann, N. F.: Prediction of event-based
stormwater runoff quantity and quality by ANNs developed using PMI-based
input selection, J. Hydrol., 400, 10–23,
https://doi.org/10.1016/j.jhydrol.2011.01.024, 2011. a
Khan, U. T., He, J., and Valeo, C.: River flood prediction using fuzzy neural
networks: an investigation on automated network architecture, Water Sci.
Technol., 2017, 238–247, https://doi.org/10.2166/wst.2018.107, 2018. a
Lauzon, N., Anctil, F., and Baxter, C. W.: Clustering of heterogeneous precipitation fields for the assessment and possible improvement of lumped neural network models for streamflow forecasts, Hydrol. Earth Syst. Sci., 10, 485–494, https://doi.org/10.5194/hess-10-485-2006, 2006. a
Li, J., Zhang, C., Zhang, X., He, H., Liu, W., and Chen, C.: Temperature
Compensation of Piezo-Resistive Pressure Sensor Utilizing Ensemble AMPSO-SVR
Based on Improved Adaboost.RT, IEEE Access, 8, 12413–12425,
https://doi.org/10.1109/ACCESS.2020.2965150, 2020. a
Liu, S., Xu, J., Zhao, J., Xie, X., and Zhang, W.: Efficiency enhancement of a
process-based rainfall–runoff model using a new modified AdaBoost.RT
technique, Appl. Soft Comput., 23, 521–529,
https://doi.org/10.1016/j.asoc.2014.05.033, 2014. a
López, V., Fernández, A., García, S., Palade, V., and
Herrera, F.: An insight into classification with imbalanced data: Empirical
results and current trends on using data intrinsic characteristics,
Inform. Sciences, 250, 113–141, https://doi.org/10.1016/j.ins.2013.07.007, 2013. a
Maier, H. R. and Dandy, G. C.: Neural networks for the prediction and
forecasting of water resources variables: A review of modelling issues and
applications, Environ. Modell. Softw., 15, 101–124,
https://doi.org/10.1016/S1364-8152(99)00007-9, 2000. a
Maier, H. R., Jain, A., Dandy, G. C., and Sudheer, K.: Methods used for the
development of neural networks for the prediction of water resource variables
in river systems: Current status and future directions, Environ.
Modell. Softw., 25, 891–909, https://doi.org/10.1016/j.envsoft.2010.02.003,
2010. a, b
Moniz, N., Ribeiro, R., Cerqueira, V., and Chawla, N.: SMOTEBoost for
Regression: Improving the Prediction of Extreme Values, in: 2018 IEEE 5th
International Conference on Data Science and Advanced Analytics (DSAA),
150–159, IEEE, https://doi.org/10.1109/DSAA.2018.00025, 2018. a
Mosavi, A., Ozturk, P., and Chau, K.-w.: Flood Prediction Using Machine
Learning Models: Literature Review, Water, 10, 1536,
https://doi.org/10.3390/w10111536, 2018. a
Ni, L., Wang, D., Wu, J., Wang, Y., Tao, Y., Zhang, J., and Liu, J.:
Streamflow forecasting using extreme gradient boosting model coupled with
Gaussian mixture model, J. Hydrol., 586, 124901,
https://doi.org/10.1016/j.jhydrol.2020.124901, 2020. a
Nirupama, N., Armenakis, C., and Montpetit, M.: Is flooding in Toronto a
concern?, Nat. Hazards, 72, 1259–1264, https://doi.org/10.1007/s11069-014-1054-2,
2014. a
Ouarda, T. B. M. J. and Shu, C.: Regional low-flow frequency analysis using
single and ensemble artificial neural networks, Water Resour. Res.,
45, W11428, https://doi.org/10.1029/2008WR007196, 2009. a, b
Papacharalampous, G., Tyralis, H., Langousis, A., Jayawardena, A. W.,
Sivakumar, B., Mamassis, N., Montanari, A., and Koutsoyiannis, D.:
Probabilistic hydrological post-processing at scale: Why and how to apply
machine-learning quantile regression algorithms, Water, 11,
2126, https://doi.org/10.3390/w11102126, 2019. a
Pisa, I., Santín, I., Vicario, J. L., Morell, A., and Vilanova, R.: Data
preprocessing for ANN-based industrial time-series forecasting with
imbalanced data, in: European Signal Processing Conference, 2019,
European Signal Processing Conference, EUSIPCO,
https://doi.org/10.23919/EUSIPCO.2019.8902682, 2019. a
Razali, N., Ismail, S., and Mustapha, A.: Machine learning approach for flood
risks prediction, IAES International Journal of Artificial Intelligence, 9,
73–80, https://doi.org/10.11591/ijai.v9.i1.pp73-80, 2020. a, b
Saffarpour, S., Erechtchoukova, M. G., Khaiter, P. A., Chen, S. Y., and Heralall, M.: Short-term prediction of flood events in a small urbanized watershed using multi-year hydrological records, in: Proceedings of the 21st International Congress on Modelling and Simulation (MODSIM2015), Broadbeach, Australia, 29 November–4 December 2014, 2234–2240, https://doi.org/10.36334/MODSIM.2015.L7.saffarpour, 2015. a
Seibert, S. P., Ehret, U., and Zehe, E.: Disentangling timing and amplitude errors in streamflow simulations, Hydrol. Earth Syst. Sci., 20, 3745–3763, https://doi.org/10.5194/hess-20-3745-2016, 2016. a
Seiffert, C., Khoshgoftaar, T. M., Van Hulse, J., and Napolitano, A.:
Resampling or reweighting: A comparison of boosting implementations, in:
Proceedings – International Conference on Tools with Artificial Intelligence,
ICTAI, 1, 445–451, https://doi.org/10.1109/ICTAI.2008.59, 2008. a
Sharkey, A. J. C.: On Combining Artificial Neural Nets, Connection Science,
8, 299–314, https://doi.org/10.1080/095400996116785, 1996. a, b
Sharma, A.: Seasonal to interannual rainfall probabilistic forecasts for
improved water supply management: Part 1 — A strategy for system predictor
identification, J. Hydrol., 239, 232–239,
https://doi.org/10.1016/S0022-1694(00)00346-2, 2000. a
Shrestha, D. L. and Solomatine, D. P.: Experiments with AdaBoost.RT, an
improved boosting scheme for regression, Neural Computat., 18, 1678–1710,
https://doi.org/10.1162/neco.2006.18.7.1678, 2006. a, b, c, d
Shu, C. and Ouarda, T. B.: Flood frequency analysis at ungauged sites using
artificial neural networks in canonical correlation analysis physiographic
space, Water Resour. Res., 43, W07438, https://doi.org/10.1029/2006WR005142, 2007. a
Solomatine, D. P. and Ostfeld, A.: Data-driven modelling: some past
experiences and new approaches, J. Hydroinform., 10, 3–22,
https://doi.org/10.2166/hydro.2008.015, 2008. a
Sudheer, K. P., Nayak, P. C., and Ramasastri, K. S.: Improving peak flow
estimates in artificial neural network river flow models, Hydrol.
Process., 17, 677–686, https://doi.org/10.1002/hyp.5103, 2003. a, b, c
Sufi Karimi, H., Natarajan, B., Ramsey, C. L., Henson, J., Tedder, J. L., and
Kemper, E.: Comparison of learning-based wastewater flow prediction
methodologies for smart sewer management, J. Hydrol., 577, 123977,
https://doi.org/10.1016/j.jhydrol.2019.123977, 2019. a
Tiwari, M. K. and Chatterjee, C.: Uncertainty assessment and ensemble flood
forecasting using bootstrap based artificial neural networks (BANNs),
J. Hydrol., 382, 20–33, https://doi.org/10.1016/j.jhydrol.2009.12.013, 2010. a
Tongal, H. and Booij, M. J.: Simulation and forecasting of streamflows using
machine learning models coupled with base flow separation, J.
Hydrol., 564, 266–282, https://doi.org/10.1016/j.jhydrol.2018.07.004, 2018. a, b, c
Torgo, L., Ribeiro, R. P., Pfahringer, B., and Branco, P.: SMOTE for
regression, in: Lecture Notes in Computer Science (including subseries
Lecture Notes in Artificial Intelligence and Lecture Notes in
Bioinformatics), 8154 LNAI, 378–389,
https://doi.org/10.1007/978-3-642-40669-0_33, 2013. a, b
Toronto and Region Conservation Authority: Lower Don River West Remedial
Flood Protection Project, available at:
https://trca.ca/conservation/green-infrastructure/lower-don-river-west-remedial-flood-protection-project/ (last access: 12 May 2021),
2020a. a
Toth, E.: Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions, Hydrol. Earth Syst. Sci., 20, 2383–2394, https://doi.org/10.5194/hess-20-2383-2016, 2016. a, b
Vezhnevets, A. and Barinova, O.: Avoiding Boosting Overfitting by Removing
Confusing Samples, in: Machine Learning: ECML 2007, 4701 LNAI,
430–441, Springer Berlin Heidelberg, Berlin, Heidelberg,
https://doi.org/10.1007/978-3-540-74958-5_40, 2007. a
Wang, R., Zhang, X., and Li, M. H.: Predicting bioretention pollutant removal
efficiency with design features: A data-driven approach, J.
Environ. Manage., 242, 403–414, https://doi.org/10.1016/j.jenvman.2019.04.064,
2019a. a
Wang, S.-H., Li, H.-F., Zhang, Y.-J., and Zou, Z.-S.: A Hybrid Ensemble Model
Based on ELM and Improved AdaBoost.RT Algorithm for Predicting the Iron Ore
Sintering Characters, Comput. Intel. Neurosc., 2019,
1–11, https://doi.org/10.1155/2019/4164296, 2019b. a
Wang, W., Gelder, P. H., Vrijling, J. K., and Ma, J.: Forecasting daily
streamflow using hybrid ANN models, J. Hydrol., 324, 383–399,
https://doi.org/10.1016/j.jhydrol.2005.09.032, 2006.
a, b
Worland, S. C., Farmer, W. H., and Kiang, J. E.: Improving predictions of
hydrological low-flow indices in ungaged basins using machine learning,
Environ. Modell. Softw., 101, 169–182,
https://doi.org/10.1016/j.envsoft.2017.12.021, 2018. a
Wu, Y., Ding, Y., and Feng, J.: SMOTE-Boost-based sparse Bayesian model for
flood prediction, Eurasip J. Wirel. Comm.,
2020, 78, https://doi.org/10.1186/s13638-020-01689-2, 2020. a, b
Yap, B. W., Rani, K. A., Rahman, H. A. A., Fong, S., Khairudin, Z., and
Abdullah, N. N.: An Application of Oversampling, Undersampling, Bagging and
Boosting in Handling Imbalanced Datasets, Lect. Notes Electr.
Engr., 285 LNEE, 13–22, https://doi.org/10.1007/978-981-4585-18-7_2, 2014. a, b, c
Zhan, C., Han, J., Zou, L., Sun, F., and Wang, T.: Heteroscedastic and
symmetric efficiency for hydrological model evaluation criteria, Hydrol.
Res., 50, 1189–1201, https://doi.org/10.2166/nh.2019.121, 2019. a
Zhang, H., Yang, Q., Shao, J., and Wang, G.: Dynamic Streamflow Simulation via
Online Gradient-Boosted Regression Tree, J. Hydrol. Eng.,
24, 04019041, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001822, 2019. a
Zhang, Z.-L., Luo, X.-G., Yu, Y., Yuan, B.-W., and Tang, J.-F.: Integration of
an improved dynamic ensemble selection approach to enhance one-vs-one
scheme, Eng. Appl. Artif. Intel., 74, 43–53,
https://doi.org/10.1016/j.engappai.2018.06.002, 2018. a
Zhaowei, Q., Haitao, L., Zhihui, L., and Tao, Z.: Short-Term Traffic Flow
Forecasting Method With M-B-LSTM Hybrid Network, IEEE Transactions on
Intelligent Transportation Systems, 1–11,
https://doi.org/10.1109/TITS.2020.3009725, 2020. a
Short summary
Flow distributions are highly skewed, resulting in low prediction accuracy of high flows when using artificial neural networks for flood forecasting. We investigate the use of resampling and ensemble techniques to address the problem of skewed datasets to improve high flow prediction. The methods are implemented both independently and in combined, hybrid techniques. This research presents the first analysis of the effects of combining these methods on high flow prediction accuracy.
Flow distributions are highly skewed, resulting in low prediction accuracy of high flows when...
