Various Artificial Intelligence (AI) techniques in water resource management highlight the
current methodologies' strengths and limitations in forecasting, optimization, and control. We …

Wastewater treatment plants (WWTPs) are energy-intensive facilities that fulfill stringent
effluent quality norms. Energy consumption prediction in WWTPs is crucial for cost savings …

Hydrologic models require atmospheric, dynamic and static models to simulate river flow
from catchments. Thus the accuracy of hydrologic modelling highly depends on the data …

The Rainfall-Runoff (RR) relationship is essential to the hydrological cycle. Sophisticated
hydrological models can accurately investigate RR relationships; however, they require …

Hydrologic models to simulate river flows are computationally costly. In addition to the
precipitation and other meteorological time series, catchment characteristics, including soil …

Measurement inaccuracies and the absence of precise parameters value in conceptual and
analytical models pose challenges in simulating the rainfall–runoff modeling (RRM) …

This study addresses the issue of soil cadmium pollution and the common problem of data
missing during the training of artificial neural network models. A substantial amount of …

Automated fruit identification is always challenging due to its complex nature. Usually, the
fruit types and sub-types are location-dependent; thus, manual fruit categorization is also still …

River flow modeling plays a leading role in the management of water resources and
ensuring sustainability. The complex nature of hydrological systems and the difficulty in the …

Accurate monthly runoff prediction is significant to extreme flood control and water resources
management. However, traditional statistical models without multi-variable input may fail to …