The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s

Abstract This study investigated the treatment of textile wastewater contaminated with Acid Black 210 dye (AB210) using zinc oxide nanoparticles (ZnO NPs) through adsorption and photocatalytic techniques. ZnO NPs were synthesized using a green synthesis process involving eucalyptus leaves as reducing and capping agents. The synthesized ZnO NPs were characterized using UV-Vis spectroscopy, SEM, EDAX, XRD, BET, Zeta potential, and FTIR techniques. The BET analysis revealed a specific surface area and total pore volume of 26.318 m2/g. SEM images confirmed the crystalline and spherical nature of the particles, with a particle size of 73.4 nm. A photoreactor was designed to facilitate the photo-degradation process. The study investigated the inf

This paper aims to propose a hybrid approach of two powerful methods, namely the differential transform and finite difference methods, to obtain the solution of the coupled Whitham-Broer-Kaup-Like equations which arises in shallow-water wave theory. The capability of the method to such problems is verified by taking different parameters and initial conditions. The numerical simulations are depicted in 2D and 3D graphs. It is shown that the used approach returns accurate solutions for this type of problems in comparison with the analytic ones.