74 citations as recorded by crossref.

1. Uncertain least square support vector regression with imprecise observations H. Zhang & Y. Sheng 10.3233/JIFS-236849
2. Long-term variation analysis of a tropical river’s annual streamflow regime over a 50-year period M. Seyam & F. Othman 10.1007/s00704-014-1225-9
3. The river runoff forecast based on the modeling of time series R. Nigam et al. 10.3103/S1068373914110053
4. Evaluation of stochastic and artificial intelligence models in modeling and predicting of river daily flow time series P. Aghelpour & V. Varshavian 10.1007/s00477-019-01761-4
5. RBFNN Versus Empirical Models for Lag Time Prediction in Tropical Humid Rivers M. Seyam et al. 10.1007/s11269-016-1518-0
6. Towards Safer Data-Driven Forecasting of Extreme Streamflows J. Matos et al. 10.1007/s11269-017-1834-z
7. A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin S. Patel & P. Ramachandran 10.1007/s11269-014-0705-0
8. Space–time forecasting of groundwater level using a hybrid soft computing model M. Sahoo et al. 10.1080/02626667.2016.1252986
9. An EMD-Based Chaotic Least Squares Support Vector Machine Hybrid Model for Annual Runoff Forecasting X. Zhao et al. 10.3390/w9030153
10. Prediction of uniaxial compressive strength of carbonate rocks from nondestructive tests using multivariate regression and LS-SVM methods S. Çelik 10.1007/s12517-019-4307-2
11. Reply to the “Discussion by Haddad et al. on ‘Hydroclimatic stream flow prediction using least square-support vector regression’ by Bhagwat and Maity (2013)” P. Bhagwat & R. Maity 10.1080/09715010.2014.881075
12. Hybrid artificial intelligence-time series models for monthly streamflow modeling S. Mehdizadeh et al. 10.1016/j.asoc.2019.03.046
13. The back propagation based on the modified group method of data-handling network for oilfield production forecasting J. Guo et al. 10.1007/s13202-018-0582-9
14. Prediction of CO2 adsorption on different activated carbons by hybrid group method of data-handling networks and LSSVM L. Zhou 10.1080/15567036.2018.1548521
15. Hourly stream flow prediction in tropical rivers by multi-layer perceptron network M. Seyam & F. Othman 10.5004/dwt.2017.21510
16. Hybrid particle swarm optimization and group method of data handling for short-term prediction of natural daily streamflows D. Souza et al. 10.1007/s40808-022-01466-8
17. Novel hybrid approaches based on evolutionary strategy for streamflow forecasting in the Chellif River, Algeria M. Zakhrouf et al. 10.1007/s11600-019-00380-5
18. Estimation of Failure Rate in Water Distribution Network Using Fuzzy Clustering and LS-SVM Methods M. Aydogdu & M. Firat 10.1007/s11269-014-0895-5
19. Early detection of riverine flooding events using the group method of data handling for the Bow River, Alberta, Canada M. Elkurdy et al. 10.1080/15715124.2021.1906261
20. Statistical Estimation of Uniaxial Compressive Strength in Geotechnical Projects Using Regression Analysis: A Comparative Study R. Verma et al. 10.1061/IJGNAI.GMENG-9384
21. SVR-based prediction of evaporation combined with chaotic approach Ö. Baydaroğlu & K. Koçak 10.1016/j.jhydrol.2013.11.008
22. Monthly Mean Streamflow Prediction Based on Bat Algorithm-Support Vector Machine B. Xing et al. 10.1061/(ASCE)HE.1943-5584.0001269
23. Coupling Singular Spectrum Analysis with Least Square Support Vector Machine to Improve Accuracy of SPI Drought Forecasting Q. Pham et al. 10.1007/s11269-020-02746-7
24. An Index Used to Evaluate the Applicability of Mid-to-Long-Term Runoff Prediction in a Basin Based on Mutual Information S. Xie et al. 10.3390/w16111619
25. Scour Detection with Monitoring Methods and Machine Learning Algorithms—A Critical Review S. Tola et al. 10.3390/app13031661
26. Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons Z. Yaseen et al. 10.1007/s11269-018-1909-5
27. Modeling river discharge time series using support vector machine and artificial neural networks M. Ghorbani et al. 10.1007/s12665-016-5435-6
28. Application of Acoustic tomographic data to the short-term streamflow forecasting using data-driven methods and discrete wavelet transform Y. Olfatmiri et al. 10.1016/j.jhydrol.2022.127739
29. Uncovering global climate fields causing local precipitation extremes K. Chanda & R. Maity 10.1080/02626667.2015.1006232
30. New model for prediction binary mixture of antihistamine decongestant using artificial neural networks and least squares support vector machine by spectrophotometry method S. Mofavvaz et al. 10.1016/j.saa.2017.04.001
31. A New Method to Assess the Flow Regime Alterations in Riverine Ecosystems X. Yin et al. 10.1002/rra.2817
32. Medium- and long-term runoff forecasting based on a random forest regression model C. Shijun et al. 10.2166/ws.2020.214
33. Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines M. Seyam et al. 10.1051/matecconf/201711101007
34. Seasonal Time Series Forecasting by F1-Fuzzy Transform F. Di Martino & S. Sessa 10.3390/s19163611
35. Hybrid models to improve the monthly river flow prediction: Integrating artificial intelligence and non-linear time series models F. Fathian et al. 10.1016/j.jhydrol.2019.06.025
36. Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network A. Ahmadi et al. 10.1080/02626667.2019.1610565
37. New formulation for forecasting streamflow: evolutionary polynomial regression vs. extreme learning machine M. Rezaie-Balf & O. Kisi 10.2166/nh.2017.283
38. Comparative assessment of time series and artificial intelligence models to estimate monthly streamflow: A local and external data analysis approach S. Mehdizadeh et al. 10.1016/j.jhydrol.2019.124225
39. A Comparative Study of Autoregressive, Autoregressive Moving Average, Gene Expression Programming and Bayesian Networks for Estimating Monthly Streamflow S. Mehdizadeh & A. Kozekalani Sales 10.1007/s11269-018-1970-0
40. Enhancing the Performance of Building Load Forecasting Using Hybrid of GLSSVM – ABC Model M. Mat Daut et al. 10.1051/matecconf/20167010010
41. A comparative study of solo and hybrid data driven models for predicting bridge pier scour depth K. Qaderi et al. 10.1080/1064119X.2020.1735589
42. A geospatial approach for understanding the spatio-temporal variability and projection of future trend in groundwater availability in the Tawi basin, Jammu, India S. Roy et al. 10.1016/j.gsd.2023.100912
43. Estimates of reference evapotranspiration in the municipality of Ariquemes (RO) using neural networks GMDH-type R. Carvalho & A. Delgado 10.1590/1807-1929/agriambi.v23n5p324-329
44. Hybrid Model for Short-Term Water Demand Forecasting Based on Error Correction Using Chaotic Time Series S. Wu et al. 10.3390/w12061683
45. The Influence of Accurate Lag Time Estimation on the Performance of Stream Flow Data-driven Based Models M. Seyam & F. Othman 10.1007/s11269-014-0628-9
46. Trend analysis and ARIMA modelling of recent groundwater levels in the White Volta River basin of Ghana A. Gibrilla et al. 10.1016/j.gsd.2017.12.006
47. Improving Forecasting Accuracy of Streamflow Time Series Using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques A. Kalteh 10.1007/s11269-015-1188-3
48. Time Series Analysis of Groundwater Levels and Projection of Future Trend G. Patle et al. 10.1007/s12594-015-0209-4
49. Assessment of Normalization of Monthly Runoff Probabilistic Distribution impact on SVM and ANN Models Performance in Monthly River Flows Simulation (A Case Study: ZarrinehRud River Basin) M. Isazadeh et al. 10.29252/jwmr.8.16.22
50. Simulation of Methyl Tertiary Butyl Ether Concentrations in River-Reservoir Systems Using Support Vector Regression M. Aboutalebi et al. 10.1061/(ASCE)IR.1943-4774.0001007
51. Univariate streamflow forecasting using commonly used data-driven models: literature review and case study Z. Zhang et al. 10.1080/02626667.2018.1469756
52. Monthly Streamflow Forecasting Using Convolutional Neural Network X. Shu et al. 10.1007/s11269-021-02961-w
53. Robust Forecasting of River-Flow Based on Convolutional Neural Network C. Huang et al. 10.1109/TSUSC.2020.2983097
54. Detecting and predicting the impact of land use changes on groundwater quality, a case study in Northern Kelantan, Malaysia T. Sheikhy Narany et al. 10.1016/j.scitotenv.2017.04.171
55. Hydrographical Flow Modelling of the River Severn Using Particle Swarm Optimization I. Kenny 10.1093/comjnl/bxz106
56. Investigating the capability of two hybrid intelligence methods to predict bedform dimensions of alluvial channels K. Qaderi et al. 10.2166/ws.2017.230
57. A comparison between direct and indirect frameworks to evaluate impacts of climate change on streamflows: case study of Karkheh River basin in Iran M. Nasseri et al. 10.2166/wcc.2017.043
58. Evaluating GMDH-based models to predict daily dew point temperature (case study of Kerman province) K. Qaderi et al. 10.1007/s00703-019-00712-6
59. A runoff probability density prediction method based on B-spline quantile regression and kernel density estimation Y. He et al. 10.1016/j.apm.2020.12.043
60. Analysis of rainfall intensity impact on the lag time estimation in tropical humid rivers e. Seyam 10.21833/ijaas.2017.010.003
61. Regional Analysis of Flow Duration Curves through Support Vector Regression M. Vafakhah & S. Khosrobeigi Bozchaloei 10.1007/s11269-019-02445-y
62. A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction M. Ghorbani et al. 10.1007/s12665-015-5096-x
63. River flow modelling: comparison of performance and evaluation of uncertainty using data-driven models and conceptual hydrological model Z. Zhang et al. 10.1007/s00477-018-1536-y
64. A review on applications of ANN and SVM for building electrical energy consumption forecasting A. Ahmad et al. 10.1016/j.rser.2014.01.069
65. Data-Driven Approach for Rainfall-Runoff Modelling Using Equilibrium Optimizer Coupled Extreme Learning Machine and Deep Neural Network B. Roy et al. 10.3390/app11136238
66. Latest Advancements in Solar Photovoltaic-Thermoelectric Conversion Technologies: Thermal Energy Storage Using Phase Change Materials, Machine Learning, and 4E Analyses H. Alghamdi et al. 10.1155/2024/1050785
67. Comparison of strategies for multi-step ahead photovoltaic power forecasting models based on hybrid group method of data handling networks and least square support vector machine M. De Giorgi et al. 10.1016/j.energy.2016.04.020
68. GMDH-based semi-supervised feature selection for customer classification J. Xiao et al. 10.1016/j.knosys.2017.06.018
69. Comparison study of artificial intelligence method for short term groundwater level prediction in the northeast Gachsaran unconfined aquifer A. Khedri et al. 10.2166/ws.2020.015
70. Estimating Daily Rice Crop Evapotranspiration in Limited Climatic Data and Utilizing the Soft Computing Algorithms MLP, RBF, GRNN, and GMDH P. Aghelpour et al. 10.1155/2022/4534822
71. Review of the limitations and potential empirical improvements of the parametric group method of data handling for rainfall modelling R. Lake et al. 10.1007/s11356-022-23194-3
72. Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting R. Abrahart et al. 10.1177/0309133312444943
73. Investigating the application of artificial intelligence for earthquake prediction in Terengganu S. Marhain et al. 10.1007/s11069-021-04716-7
74. Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms H. Erdal & O. Karakurt 10.1016/j.jhydrol.2012.11.015