River flow (Q flow) is a hydrological process that considerably impacts the management and
sustainability of water resources. The literature has shown great potential for nature-inspired …

The management of water resources depends heavily on hydrological prediction, and
advances in machine learning (ML) present prospects for improving predictive modelling …

Biomass waste must be treated and disposed off properly, as it concerns the possibility of
deposition in the environment. This could worsen the ecosystem by promoting the growth of …

The purpose of the study was to use hierarchical clustering and Thiessen polygon
algorithms to identify the significant rain gauge stations for flood forecasting at Sardar …

Current research studies offer an investigation of machine learning methods used for
forecasting rainfall in urban metropolitan cities. Time series data, distinguished by their …

Climate change is a serious global issue causing more extreme weather patterns, resulting
in more frequent and severe events like urban flooding. This review explores the connection …

The increasing carbon emissions in Malaysia necessitate accurate methods to track and
control pollution levels. This study focuses on predicting carbon monoxide (CO) …

The prediction of food shelf life has become a vital tool for distributors and consumers,
enabling them to determine storage and optimal edible time, thus avoiding unexpected food …

Streamflow forecasting is essential for effective water resource planning and early warning
systems. Streamflow and related parameters are often characterized by uncertainties and …

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 …