Study of the effectiveness of wavelet genetic programming model for water quality analysis in the Uttar Pradesh region.

Abstract

Water constitutes an essential part of the earth as it helps in making the environment greener and support life. But water quality and availability are drastically affected by rising water pollution and its poor sanitation. Water gets contaminated due to the excessive use of chemicals by the industries, fertilizers, and pesticides by the farmers. Not only the surface water, groundwater and river water are also getting contaminated. Several published work in Indian context have used different models for the prediction of water quality. Some of them performed poorly due to the presence of irrelevant and missing data in the training samples. Moreover, these studies have assessed water quality on the basis of biochemical oxygen demand (BOD) and coliform and chemical oxygen demand (COD), whereas dissolved oxygen(DO) is one of the most important parameters in terms of water quality assessment as it is considered a key determinant of pollution. Thus, there is a strong need to categorically identify and visualize the DO as one of the key components responsible for deteriorating the quality of water in Indian context. The main objective of this work is to build a wavelet genetic programming (WGP)-based workflow model for the assessment of water quality in 13 rivers of Uttar Pradesh region. WGP model has a unique feature of discarding the redundant and irrelevant data values from the source data. The proposed WGP model has given promising results which can be attributed to two factors: firstly, the novel use of Morlet wavelet in place of the widely popular Db wavelet, as the mother wavelet, and secondly, the use of MICE technique for missing value imputation in the pre-processing stage. The proposed model not only cleans the data but also demonstrates the feasibility of using DO values as one of the prime factors to assess the water quality.