Application of random forest for modelling of surface water salinity

Modeling surface water quality using artificial intelligence-based models is essential in projecting suitable mitigation measures. However, it remains a challenge and requires further research to enhance the modeling accuracy. For this aim, this article presents a methodology to optimize the modeling inputs and reduce the associated complexity. The proposed approach employs Random Forest for modeling surface water salinity in terms of electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River basin, one of the major rivers in Asia. Various water quality parameters measured monthly over a historical 30-year period were utilized in the modeling process. Various statistical indicators were used to evaluate the model performance. Random Forest process is suitable technique to simulate the salinity of surface water bodies, and effective tool in minimizing the modeling complexity and elaborating proper management and mitigation measures.(c) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).

Automated summary

Modeling surface water quality using artificial intelligence-based models is essential and challenging. This article presents a methodology using Random Forest to optimize modeling inputs and reduce complexity, specifically for modeling surface water salinity in the upper Indus River basin. The proposed approach utilizes various water quality parameters measured over a 30-year period and evaluates model performance using statistical indicators.