Water quality prediction of salton sea using machine learning and big data techniques
Priyanka Chawla, Xiyu Cao, Yichen Fu, Ching-min Hu, Meng Wang, Shenquan Wang, Jerry Gao
Abstract
Water accounts for about 70% of the earth’s surface and is one of the most important sources of life. The degradation of the Salton Sea’s water quality due to excessive usage of fertilisers and pesticides in agriculture has posed serious risks to biodiversity, human health, and the local economy. As a result, it is necessary to establish effective and reliable methodologies for assessing water quality and predicting future trends. This work investigates regression and machine learning models such as linear regression, random forest, support vector machine (SVM) and long short-term memory (LSTM) to predict the salinity level of Salton Sea and forecast future trend. The proposed work studies the Salton Sea water quality and predict certain chemical or physical characteristics such as salinity, pH, and dissolved oxygen. It also finds the correlation between water indexes and environmental indexes surrounding the Salton Sea. The findings of this study can assist policymakers in determining the salinity of water and devising policies to minimise salt levels in order to manage freshwater for long-term development. While evaluating experiments, it has been found that the machine learning methodology is more flexible and accurate than traditional statistical methods, and that it aids in achieving better results.