Increasing generation of municipal solid waste, heterogeneity of waste composition, and
complex processes of waste management and recovery have limited the performance of …

The runoff prediction can provide scientific basis for flood control, disaster reduction and
water resources planning. Due to a large number of uncertainties in runoff prediction, it is …

Rainfall-runoff modeling is a challenging and important nonlinear time series problem in
hydrological sciences. Recently, among the data-driven rainfall-runoff models, those ones …

Predicting variations in stock price index has been an important application area of machine
learning research. Due to the non-linear and complex nature of the stock market making …

Mathematical modelling is an indispensable tool to support water resource recovery facility
(WRRF) operators and engineers with the ambition of creating a truly circular economy and …

Rainfall-runoff modeling, a nonlinear time series process, is challenging and important in
hydrological sciences. Among the data-driven approaches, those ones based on the long …

The accurate forecast of wastewater treatment plant (WWTP) key features can comprehend
and predict the plant behavior to support process design and controls, improve system …

This study assessed the accuracy of various methods for estimating lake evaporation in arid,
high-wind environments, leveraging water temperature data from Landsat 8. The evaluation …

Due to the comprehensive sources of urban sewage, the contents of pollutants in urban
sewage are quite complex and fluctuate frequently. The unstable status of both sewage …

The main aim of this study was to develop hybrid machine learning (ML)-based ensemble
modeling of the rainfall-runoff process in the Katar catchment, Ethiopia. This study used four …