Water Quality Monitoring with Satellite Image Using Convolutional Neural Network

Abstract

Monitoring the quality of water becomes vital in maintaining the security as well as the viability of water supplies. The manual sampling and analysis in laboratories used in conventional monitoring techniques can be time-consuming and expensive. During this era, imagery from satellites became a useful tool for assessing water quality across wide geographic areas. This paper is to assess and monitor water quality parameters using Convolutional Neural Network with 98% accuracy. It employs data acquired from satellites to detect and quantify various factors impacting water quality, such as pH, salinity, turbidity, chlorophyll and temperature. Spatial analysis differentiates water quality characteristics whereas Temporal analysis compares satellite data over time to detect changes in water quality. The proposed system offers significant advantages over traditional monitoring methods. It can be further improved by incorporating more advanced machine learning algorithms and satellite imagery.