Improvement in Explicit Prediction of Water Quality Using Wavelet-Based LSSVR and M5pRT

• Published in: Complexity (New York, N.Y.) Vol. 2021; no. 1
• Main Authors: Bhardwaj, Rashmi, Bangia, Aashima
• Format: Journal Article
• Language: English
• Published: Hoboken Hindawi 2021; Hindawi Limited; Hindawi-Wiley
• Subjects: Artificial intelligence; Case studies; Cholera; Datasets; Decomposition; Environmental protection; Goodness of fit; Groundwater; Hypertension; Infant mortality; Iodine; Neural networks; Noise reduction; Pollutants; Precipitation; Regression analysis; Rivers; Root-mean-square errors; Support vector machines; Water quality; Wavelet transforms
• ISSN: 1076-2787; 1099-0526
• DOI: 10.1155/2021/6643472

Imbalance in the pH of water reduces this precious resource as an extremely dangerous liquid for human health and plants’ growth. Change in the pH levels of the drinkable water has majorly raised concern towards diverse health issues like heart problems, infant mortality rates, pigmentation of skin, and cholera outbreaks. Therefore, it is necessary to keep a check on essential water quality components that include acidic/basic nature of water. As per the US Environmental Protection Agency (USEPA), the drinkable water should have a pH level ranging from 6.5 to 8.5. Two sample situates have been identified wherever highly reported pollutants levels were found and have been analyzed through artificial intelligence (AI) techniques. It can be observed that wavelet denoised signals fed into the least squares support vector regression (LSSVR) and M5 prime regression tree (M5pRT) predicted more accurately on the basis of the performance errors that are as follows: (a) root mean squared error (RMSE); (b) mean squared error (MSE); (c) mean absolute error (MAE). On the basis of these errors, the coefficient of determination/goodness of fit (R2) simulated for the prototypes is developed in this study. RMSE outcomes diminish on the whole on applying the training and forecasting data-division via WLSSVR and WM5pRT as compared with fitting the normalized data through LSSVR and M5pRT. These performance measures are essential to analyze the concentration levels of pH in the river streams at the identified sites of study. Thus, the observed pattern from this study may help for future estimation of the quality of water at their sources so that it prohibits the further increase in either acidic or basic salts which prove to be lethal for the environment. Thus, these predictors would be helpful towards formulation of strategies for protection of ecosystem and human health.