Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research …
River flow (Q flow) is a hydrological process that considerably impacts the management and
sustainability of water resources. The literature has shown great potential for nature-inspired …
sustainability of water resources. The literature has shown great potential for nature-inspired …
Enhancing short-term forecasting of daily precipitation using numerical weather prediction bias correcting with XGBoost in different regions of China
Accurate precipitation (P) short-term forecasts are important for engineering studies and
water allocation. This study evaluated a method for bias correction of the Numerical Weather …
water allocation. This study evaluated a method for bias correction of the Numerical Weather …
Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as …
Monthly streamflow prediction is very important for many hydrological applications in
providing information for optimal use of water resources. In this study, the prediction …
providing information for optimal use of water resources. In this study, the prediction …
[HTML][HTML] 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 …
A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment
In this research, monthly wind speed time series of the Kirsehir was investigated using the
stand-alone, hybrid and ensemble models. The artificial neural networks, Gaussian process …
stand-alone, hybrid and ensemble models. The artificial neural networks, Gaussian process …
Application of innovative machine learning techniques for long-term rainfall prediction
Rainfall forecasting is critical because it is the componen t that has the strongest link to
natural disasters such as landslides, floods, mass movements, and avalanches. The present …
natural disasters such as landslides, floods, mass movements, and avalanches. The present …
Implementation of hybrid particle swarm optimization-differential evolution algorithms coupled with multi-layer perceptron for suspended sediment load estimation
River suspended sediment load (SSL) estimation is of importance in water resources
engineering and hydrological modeling. In this study, a novel hybrid approach is …
engineering and hydrological modeling. In this study, a novel hybrid approach is …
Pre-and post-dam river water temperature alteration prediction using advanced machine learning models
Dams significantly impact river hydrology by changing the timing, size, and frequency of low
and high flows, resulting in a hydrologic regime that differs significantly from the natural flow …
and high flows, resulting in a hydrologic regime that differs significantly from the natural flow …
[HTML][HTML] Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil …
The study proposes an improved Harris hawks optimization (IHHO) algorithm by integrating
the standard Harris hawks optimization (HHO) algorithm and mutation-based search …
the standard Harris hawks optimization (HHO) algorithm and mutation-based search …
Application of artificial neural networks, support vector machine and multiple model-ANN to sediment yield prediction
Sediment yield is important for maintaining soil health, reservoir sustainability,
environmental pollution, and conservation of natural resources. The main aim of the present …
environmental pollution, and conservation of natural resources. The main aim of the present …