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 …

Application of machine learning and emerging remote sensing techniques in hydrology: A state-of-the-art review and current research trends

A Saha, SC Pal - Journal of Hydrology, 2024‏ - Elsevier
Water, one of the most valuable resources on Earth, is the subject of the study of hydrology,
which is of utmost importance. Satellite remote sensing (RS) has emerged as a critical tool …

Stacked machine learning algorithms and bidirectional long short-term memory networks for multi-step ahead streamflow forecasting: A comparative study

F Granata, F Di Nunno, G de Marinis - Journal of Hydrology, 2022‏ - Elsevier
Prediction of river flow rates is an essential task for both flood protection and optimal water
resource management. The high uncertainty associated with basin characteristics …

Performance comparison of an LSTM-based deep learning model versus conventional machine learning algorithms for streamflow forecasting

M Rahimzad, A Moghaddam Nia, H Zolfonoon… - Water Resources …, 2021‏ - Springer
Streamflow forecasting plays a key role in improvement of water resource allocation,
management and planning, flood warning and forecasting, and mitigation of flood damages …

An enhanced monthly runoff time series prediction using extreme learning machine optimized by salp swarm algorithm based on time varying filtering based empirical …

W Wang, Q Cheng, K Chau, H Hu, H Zang, D Xu - Journal of Hydrology, 2023‏ - Elsevier
Reliable runoff prediction plays a significant role in reservoir scheduling, water resources
management, and efficient utilization of water resources. To effectively enhance the …

Modeling streamflow in non-gauged watersheds with sparse data considering physiographic, dynamic climate, and anthropogenic factors using explainable soft …

C Madhushani, K Dananjaya, IU Ekanayake… - Journal of …, 2024‏ - Elsevier
Streamflow forecasting is essential for effective water resource planning and early warning
systems. Streamflow and related parameters are often characterized by uncertainties and …

Uncertainty analysis of climate change impacts on flood frequency by using hybrid machine learning methods

MV Anaraki, S Farzin, SF Mousavi, H Karami - Water Resources …, 2021‏ - Springer
In the present study, for the first time, a new framework is used by combining metaheuristic
algorithms, decomposition and machine learning for flood frequency analysis under climate …

[HTML][HTML] River stream flow prediction through advanced machine learning models for enhanced accuracy

N Kedam, DK Tiwari, V Kumar, KM Khedher… - Results in …, 2024‏ - Elsevier
Abstract The Narmada River basin, a significant water resource in central India, plays a
crucial role in supporting agricultural, industrial, and domestic water supply. Effective …

[HTML][HTML] Application of machine learning and process-based models for rainfall-runoff simulation in Dupage River basin, Illinois

A Bhusal, U Parajuli, S Regmi, A Kalra - Hydrology, 2022‏ - mdpi.com
Rainfall-runoff simulation is vital for planning and controlling flood control events. Hydrology
modeling using Hydrological Engineering Center—Hydrologic Modeling System (HEC …

Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms

Y Essam, YF Huang, JL Ng, AH Birima, AN Ahmed… - Scientific Reports, 2022‏ - nature.com
Floods and droughts are environmental phenomena that occur in Peninsular Malaysia due
to extreme values of streamflow (SF). Due to this, the study of SF prediction is highly …