47 citations as recorded by crossref.

1. Flood Evacuation Routes Based on Spatiotemporal Inundation Risk Assessment Y. Lee et al.
2. Dynamic Modeling of Aeroengine Rotor Speed Based on Data Fusion Method J. Hong et al.
3. Prediction of monthly regional groundwater levels through hybrid soft-computing techniques F. Chang et al.
4. Comparison between NARX-NN and HEC-HMS models to simulate Wadi Seghir catchment runoff events in Algerian northern I. Kadri et al.
5. Improving the Long Lead-Time Inundation Forecasts Using Effective Typhoon Characteristics B. Jhong et al.
6. Nonlinear autoregressive neural networks with external inputs for forecasting of typhoon inundation level H. Ouyang
7. Real-Time Urban Inundation Prediction Combining Hydraulic and Probabilistic Methods H. Kim et al.
8. Long short-term memory integrating moving average method for flood inundation depth forecasting based on observed data in urban area S. Yang et al.
9. Application of Nonlinear Time Series and Machine Learning Algorithms for Forecasting Groundwater Flooding in a Lowland Karst Area B. Basu et al.
10. Trend Analysis of Water Inflow Into the Dam Reservoirs Under Future Conditions Predicted By Dynamic NAR and NARX Models P. Pishgah Hadiyan et al.
11. Estimating spatio-temporal dynamics of stream total phosphate concentration by soft computing techniques F. Chang et al.
12. Regional Inundation Forecasting Using Machine Learning Techniques with the Internet of Things S. Yang & L. Chang
13. Using Multi-Factor Analysis to Predict Urban Flood Depth Based on Naive Bayes H. Wang et al.
14. On the Downscaling of Meteorological Fields Using Recurrent Networks for Modelling the Water Balance in a Meso-Scale Catchment Area of Saxony, Germany R. Kronenberg et al.
15. Data-Driven Approach for the Rapid Simulation of Urban Flood Prediction H. Kim & K. Han
16. Evaluation of the performance of data-driven approaches for filling monthly precipitation gaps in a semi-arid climate conditions O. Katipoğlu
17. Flood Prediction Using Machine Learning Models: Literature Review A. Mosavi et al.
18. Recurrent neural networks for rainfall-runoff modeling of small Amazon catchments L. de Mendonça et al.
19. An artificial neural network-based snow cover predictive modeling in the higher Himalayas B. Mishra et al.
20. Assessing the effective spatial characteristics of input features through physics-informed machine learning models in inundation forecasting during typhoons B. Jhong et al.
21. Machine Learning and Urban Drainage Systems: State-of-the-Art Review S. Kwon & J. Kim
22. Simultaneous hydrological prediction at multiple gauging stations using the NARX network for Kemaman catchment, Terengganu, Malaysia W. Lee & T. Tuan Resdi
23. Integration of a Parsimonious Hydrological Model with Recurrent Neural Networks for Improved Streamflow Forecasting Y. Tian et al.
24. Flood mapping based on the combination of support vector regression and Heun’s scheme J. Jang et al.
25. Development in flood forecasting: A comprehensive review of complex and machine learning models S. Refadah
26. Real-time probabilistic sediment concentration forecasting using integrated dynamic network and error distribution heterogeneity F. Zhao et al.
27. Regional flood inundation nowcast using hybrid SOM and dynamic neural networks L. Chang et al.
28. Nonlinear Autoregressive Model With Exogenous Input Recurrent Neural Network to Predict Satellites’ Clock Bias Y. Liang et al.
29. Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network Y. Zhou et al.
30. A machine learning approach for forecasting and visualising flood inundation information S. Kabir et al.
31. A deep convolutional neural network model for rapid prediction of fluvial flood inundation S. Kabir et al.
32. ARTIFICIAL NEURAL NETWORK APPLIED IN FORECASTING THE COMPOSITION OF MUNICIPAL SOLID WASTE IN IASI, ROMANIA C. Ghinea et al.
33. An integrated two-stage support vector machine approach to forecast inundation maps during typhoons B. Jhong et al.
34. Methods for Hydropower Discharge Prediction: A Review . Nurul Najwa Anuar et al.
35. Physically based vs. data-driven models for streamflow and reservoir volume prediction at a data-scarce semi-arid basin G. Özdoğan-Sarıkoç & F. Dadaser-Celik
36. A GNSS-based weather forecasting approach using Nonlinear Auto Regressive Approach with Exogenous Input (NARX) Z. Rahimi et al.
37. Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control F. Chang et al.
38. Evaluating the contribution of multi-model combination to streamflow hindcasting by empirical and conceptual models Y. Chiang et al.
39. Prediction of municipal solid waste generation using nonlinear autoregressive network M. Younes et al.
40. Data-Driven Modeling of Groundwater Level with Least-Square Support Vector Machine and Spatial–Temporal Analysis Y. Tang et al.
41. Predicting Outflow Hydrographs of Potential Dike Breaches in a Bifurcating River System Using NARX Neural Networks A. Bomers
42. A Sink Screening Approach for 1D Surface Network Simplification in Urban Flood Modelling G. Zhao et al.
43. Spatial-temporal flood inundation nowcasts by fusing machine learning methods and principal component analysis L. Chang et al.
44. A reliable hybrid outlier robust non-tuned rapid machine learning model for multi-step ahead flood forecasting in Quebec, Canada I. Ebtehaj & H. Bonakdari
45. Data-Driven and Physics-Informed hybrid identification of a spacecraft simulator using optimized NARX neural networks F. Yazdiniya et al.
46. Exploring the spatio-temporal interrelation between groundwater and surface water by using the self-organizing maps I. Chen et al.
47. Suitability of ANN-Based Daily Streamflow Extension Models: a Case Study of Gaoping River Basin, Taiwan J. Shiau & H. Hsu