Environmental science and pollution research international
36976466
Rainfall is a vital process in the hydrological cycle of the globe. Accessing reliable and accurate rainfall data is crucial for water resources operation, flood control, drought warning, irrigation, and drainage. In the present study, the main objec...
According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe. However, verifying this prediction using observations has remained a substantial challenge owing to lar...
Deep learning methods exhibited significant advantages in mapping highly nonlinear relationships with acceptable computational speed, and have been widely used to predict water quality. However, various model selection and construction methods result...
This study examined and addressed climate change's effects on hydrological patterns, particularly in critical places like the Godavari River basin. This study used daily gridded rainfall and temperature datasets from the Indian Meteorological Departm...
Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Accurate and timely warnings are critical for mitigating flood risks, but hydrological simula...
A deep learning architecture, denoted as CNNsLSTM, is proposed for hourly rainfall-runoff modeling in this study. The architecture involves a serial coupling of the one-dimensional convolutional neural network (1D-CNN) and the long short-term memory ...
Water science and technology : a journal of the International Association on Water Pollution Research
38747954
With the widespread application of machine learning in various fields, enhancing its accuracy in hydrological forecasting has become a focal point of interest for hydrologists. This study, set against the backdrop of the Haihe River Basin, focuses on...
Dams and reservoirs have significantly altered river flow dynamics worldwide. Accurately representing reservoir operations in hydrological models is crucial yet challenging. Detailed reservoir operation data is often inaccessible, leading to relying ...
The estimation and prediction of the amount of sediment accumulated in reservoirs are imperative for sustainable reservoir sedimentation planning and management and to minimize reservoir storage capacity loss. The main objective of this study was to ...
Hydrological forecasting is of great importance for water resources management and planning, especially given the increasing occurrence of extreme events such as floods and droughts. The physics-informed machine learning (PIML) models effectively int...