A review on the applications of machine learning for runoff modeling
B Mohammadi - Sustainable Water Resources Management, 2021 - Springer
The growing menace of global warming and restrictions on access to water in each region is
a huge threat to global hydrological sustainability. Hence, the perspective at which …
a huge threat to global hydrological sustainability. Hence, the perspective at which …
Study on optimization and combination strategy of multiple daily runoff prediction models coupled with physical mechanism and LSTM
J Guo, Y Liu, Q Zou, L Ye, S Zhu, H Zhang - Journal of Hydrology, 2023 - Elsevier
Accurate prediction of runoff is an important foundation for optimizing water resource
allocation and reservoir scheduling operations. However, due to its complex characteristics …
allocation and reservoir scheduling operations. However, due to its complex characteristics …
An integrated statistical-machine learning approach for runoff prediction
Nowadays, great attention has been attributed to the study of runoff and its fluctuation over
space and time. There is a crucial need for a good soil and water management system to …
space and time. There is a crucial need for a good soil and water management system to …
Convergence of mechanistic modeling and artificial intelligence in hydrologic science and engineering
Hydrology is a mature physical science based on application of first principles. However, the
water system is complex and its study requires analysis of increasingly large data available …
water system is complex and its study requires analysis of increasingly large data available …
[HTML][HTML] DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling
Despite the considerable success of deep learning methods in modelling physical
processes, they suffer from a variety of issues such as overfitting and lack of interpretability …
processes, they suffer from a variety of issues such as overfitting and lack of interpretability …
Optimal design and feature selection by genetic algorithm for emotional artificial neural network (EANN) in rainfall-runoff modeling
Rainfall-runoff (rr) modeling at different time scales is considered as a significant issue in
hydro-environmental planning. As a first hydrological implementation, for one-time-ahead rr …
hydro-environmental planning. As a first hydrological implementation, for one-time-ahead rr …
Improving river routing using a differentiable Muskingum‐Cunge model and physics‐informed machine learning
Recently, rainfall‐runoff simulations in small headwater basins have been improved by
methodological advances such as deep neural networks (NNs) and hybrid physics‐NN …
methodological advances such as deep neural networks (NNs) and hybrid physics‐NN …
Daily streamflow forecasting in Sobradinho Reservoir using machine learning models coupled with wavelet transform and bootstrap**
Improving forecasting techniques for streamflow time series is of extreme importance for
water resource planning. Among the available techniques, those based on machine …
water resource planning. Among the available techniques, those based on machine …
Inclusive multiple model using hybrid artificial neural networks for predicting evaporation
Predicting evaporation is essential for managing water resources in basins. Improvement of
the prediction accuracy is essential to identify adequate inputs on evaporation. In this study …
the prediction accuracy is essential to identify adequate inputs on evaporation. In this study …
Modeling of stage-discharge using back propagation ANN-, ANFIS-, and WANN-based computing techniques
The development of the stage-discharge relationship is a fundamental issue in hydrological
modeling. Due to the complexity of the stage-discharge relationship, discharge prediction …
modeling. Due to the complexity of the stage-discharge relationship, discharge prediction …