A Hybrid Machine Learning Framework for River Water Quality Prediction under Data Uncertainties

Abstract

The impact of climate change on water quality variables is an essential topic for sustainable river water quality management in a warming environment and is a great environmental concern worldwide. River Water Quality (RWQ) models aim to simulate the behavior of various water quality variables in response to pollutants, land use changes, and climate change. However, these water quality models suffer from sparse data leading to data uncertainty. In the past decades, different models have been successfully used for RWQ modeling under different spatial and temporal scales. To simulate RWQ variables, physically based water quality models can be used, but they require large amounts of site-specific detailed data, including stream geometry, meteorological variables, and hydraulic properties of the river, which are unavailable for many river systems globally. However, unlike process-based models, statistical models possess many advantages. Additionally, statistical models do not require a large number of input variables, which are unavailable for many ungauged river systems. However, accurately describing the nonlinear characteristics of a data series is a significant shortcoming of this approach. To overcome such limitations, artificial intelligence algorithms, i.e., Machine Learning (ML) techniques, are widely used to address a range of nonlinear prediction problems. Such models are suited for information extraction from sequential data in RWQ modeling, and they serve functionalities to build models using a reduced number of variables with more accurate simulation. newlineMachine Learning (ML) has been increasingly adopted due to its ability to model complex and nonlinearities between river water quality (RWQ) variables and their predictors (e.g., Air Temperature, AT, streamflow). To simulate RWQ parameters using data-driven algorithms, more input variables are required, which are unavailable for many ungauged river systems. Climatic variables that are readily available are the maximum, minimum, and average AT to build RWQ m