A stacking ANN ensemble model of ML models for stream water quality prediction of Godavari River Basin, India

摘要: The importance of water quality models has increased as their inputs are critical to the development of risk assessment framework for environmental management and monitoring of rivers. However, with the advent of a plethora of recent advances in ML algorithms better predictions are possible. This study proposes a causal and effect model by considering climatological such as temperature and precipitation along with geospatial information related to the agricultural land use factor (ALUF), the forest land use factor (FLUF), the grassland usage factor (GLUF), the shrub land use factor (SLUF), and the urban land use factor (ULUF). All these factors are included in the input data, whereas four Stream Water Quality parameters (SWQPs) such as Electrical Conductivity (EC), Biochemical Oxygen Demand (BOD), Nitrate, and Dissolved Oxygen (DO) from 2019 to 2021 are taken as outputs to predict the Godavari River …