Assessment Of Water Quality And Salinity Levels In The East Upputeru Catchment Of Andhra Pradesh India Using Remote Sensing and Soft Computing

Abstract

Water is like an elixir of life and an important component of all life forms. This work newlineencompasses three distinct yet interconnected research endeavours, each shedding light on newlinecritical water and soil quality assessment aspects in the left Upputeru catchment of Andhra newlinePradesh, India. The present study focuses on the groundwater quality assessment of arbitrarily newlineselected areas in the left Upputeru catchment in the delta region of the West Godavari district newlinein Andhra Pradesh for the past five years. Samples of groundwater are evaluated for their newlinephysicochemical properties using traditional techniques. The same is thereafter compared newlineusing the weighted arithmetic Water Quality Index (WQI) technique. This WQI provides an newlineoverview of its suitability for all human needs. This report also examines the effects of changes newlinein land use and land cover (LULC) and the intensive aquaculture practices in the study area. In newlineaddition, WQI and salinity interpolation mappings are created using four interpolation newlinemethods, including spline (SP), inverse distance weighting (IDW), ordinary kriging (OK), and newlineempirical Bayesian kriging (EBK). Using the test data, these methodologies are validated, and newlinethe IDW technique performed well in WQI instances and EBK in salinity. The WQI data from newline2017 to 2021 demonstrates the severe deterioration in water quality due to intense aquaculture newlineand ineffective management of water sources. This research outcome would help in planning newlinebetter water management to mitigate water quality deterioration. newlineTransitioning to the second phase, the study addresses the widespread phenomenon newlineleading to land degradation, particularly in regions with brackish inland aquaculture ponds. newlineHowever, because of the high geographical and temporal fluctuation, monitoring vast areas newlineprovides substantial challenges. This study uses remote sensing data and machine learning newlinetechniques to predict soil salinity. Four linear models, such as linear regression, Lasso, Ridge, newlineand Elastic Net regression, and three boosting algorithms