In order to improve the accuracy and stability of runoff prediction. This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA). Using this method, an integrated prediction model framework for runoff prediction was constru...
Water science and technology : a journal of the International Association on Water Pollution Research
36579879
It is critical to use research methods to collect and regulate surface water to provide water while avoiding damage. Following accurate runoff prediction, principled planning for optimal runoff is implemented. In recent years, there has been an incre...
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...
Seawater intrusion in coastal aquifers is a significant problem that can be addressed through the construction of subsurface dams or physical cut-off barriers. An alternative method is the use of microbially induced carbonate precipitation (MICP) to ...
Water science and technology : a journal of the International Association on Water Pollution Research
38747946
Instantaneous peak flows (IPFs) are often required to derive design values for sizing various hydraulic structures, such as culverts, bridges, and small dams/levees, in addition to informing several water resources management-related activities. Comp...
Water science and technology : a journal of the International Association on Water Pollution Research
38678400
Rainfall-derived inflow/infiltration (RDII) modelling during heavy rainfall events is essential for sewer flow management. In this study, two machine learning algorithms, random forest (RF) and long short-term memory (LSTM), were developed for sewer ...
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 ...
One of the important non-engineering measures for flood forecasting and disaster reduction in watersheds is the application of machine learning flood prediction models, with Long Short-Term Memory (LSTM) being one of the most representative time seri...
Macrodispersivity is critical for predicting solute behaviors with dispersive transport models. Conventional methods of estimating macrodispersivity usually need to solve flow equations and are time-consuming. Convolutional neural networks (CNN) have...