Enhanced Gradient Boosting Tree Classifier Using Optimization Technique for Water Quality Prediction

Abstract

            Reservoirs and lakes are significant sources of water. Reservoirs are important water sources for all living things. They provide healthy drinking water as well as shelter for a wide range of marine organisms. Water from such resources may be used for a variety of purposes, including manufacturing, agriculture, drinking water supplies, recreation, and aesthetic value. Reservoirs are also useful for hydroelectric power production, flood control, and scenic beauty. Water stored in such tools can also be used during a drought. Unfortunately, these essential resources are contaminated, and water quality is affected by a number of factors. Anthropogenic practices, indiscriminate sewage disposal, human activities, and industrial waste all degrade water quality. Reservoir water quality control is important for the protection of marine resources. Water quality plays a role in regulating biotic diversity, biomass, energy, and succession rate. Furthermore, polluted water can cause certain waterborne illnesses and has an effect on child mortality. It is important to evaluate various aspects of water quality in order to reduce the effects of polluted water. It could be helpful to do this by forecasting water quality parameters a few steps ahead of time. The main aim of this paper is to come up with an effective classification method for predicting water quality using variable data. In this paper, the Differential Evolution (DE) optimization algorithm is used to improve the Gradient Boosting Tree classifier.