Develo** accurate soft computing methods for groundwater level (GWL) forecasting is
essential for enhancing the planning and management of water resources. Over the past two …

There has been an unsettling rise in the river contamination due to the climate change and
anthropogenic activities. Last decades' research has immensely focussed on river basin …

The main objective of this paper is to further improve the current time-series prediction
(forecasting) algorithms based on hybrids between machine learning and nature-inspired …

Significant progress has been made in designing advanced membranes; however,
persistent challenges remain due to their reduced permeation rates and a propensity for …

In this study, three single Artificial Intelligence (AI) based models ie, Back Propagation
Neural Network (BPNN), Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector …

Solar power integration in electrical grids is complicated due to dependence on volatile
weather conditions. To address this issue, continuous research and development is required …

This study presents a hybrid approach that integrates seasonal-trend decomposition and
machine learning (termed STL-ML) for predicting the rainfall time series one step ahead …

Project delays are the major problems tackled by the construction sector owing to the
associated complexity and uncertainty in the construction activities. Artificial Intelligence (AI) …

Anthropogenic activities affect the water bodies and result in a drastic reduction of river
water quality (WQ). The development of a reliable intelligent model for evaluating the …

In recent decades, various conventional techniques have been formulated around the world
to evaluate the overall water quality (WQ) at particular locations. In the present study, back …