Air pollution is increasing for many reasons, such as the crowding of cities, the failure of
planning to consider the benefit of society and nature, and the non-implementation of …

This study compares the ability of Long Short-Term Memory (LSTM) tuned with Grey Wolf
Optimization (GWO) and machine learning models, artificial neural network (ANN), Adaptive …

Local scouring around the piers of bridges has been identified as one of the main problems
contributing to bridge failure globally. As such, the accurate prediction of safe scouring …

Growing imperviousness and urbanization have increased peak flow magnitude which
results in flood events specifically during extreme conditions. Precise and reliable multi-step …

In the present study, prediction of runoff and sediment at Polavaram and Pathagudem sites
of the Godavari basin was carried out using machine learning models such as artificial …

An explicit model management framework is introduced for predictive Groundwater Levels
(GWL), particularly suitable to Observation Wells (OWs) with sparse and possibly …

The precise prediction of groundwater level is essential for water reserve management. In
this study, the two intelligence models, viz, Artificial Neural Network (ANN) and Adaptive …

Currently, in executive comparative studies and even in the research studies of natural
resources, the use of maps produced by the geological survey forms the basis of geological …

Heavy rainfall over a short period or slowly during long periods can significantly increase the
amount of water. Where it results in floods that can pose a direct threat, capable of causing …

An assessment of uncertainty in flood hydrograph features, eg, peak discharge and flood
volume due to variability in the rainfall‐runoff model (HEC‐HMS) parameters and rainfall …