Water Quality Monitoring Using Remote-Sensing

Remote sensing techniques can be used to monitor water quality characteristics, with geographic information systems (GIS) utilizing remote sensing photos and imaging analysis results as major data sources. Remote sensing is used for weather forecasting and warning of impending cyclones in India, as well as to investigate issues such as eutrophication, oil spills, desertification, air pollution, land degradation, and deforestation. To determine water quality in coastal areas, the proposed system utilizes artificial neural networks such as the Recurrent Neural Network and ARIMA using remote sensing data, and a website is created to provide weekly updates on water quality in a particular location. While water quality monitoring based on remote sensing imagery mostly uses the semi-empirical technique, this research presents a unique technique to monitor water quality by merging high spatial resolution imageries with hyperspectral imageries (HSI). This project will benefit areas close to water bodies, and residents will be made aware of water contamination issues. The proposed technique for monitoring water quality involves merging high spatial resolution imageries with hyperspectral imageries (HSI), which is a unique approach compared to the semi-empirical technique used in water quality monitoring based on remote sensing imagery. With this technique, the individuals who live near water bodies will become more aware of the water quality in their area. The proposed system utilizes artificial neural networks such as the Recurrent Neural Network and ARIMA to determine water quality in coastal areas. The algorithms used in the system have multilayer perceptron's to achieve successful results. In addition to this, a website will be created to provide weekly updates on water quality in a specific location. The use of remote sensing techniques in monitoring water quality is not limited to just coastal areas. It can also be used to investigate issues such as eutrophication, oil spills, desertification, air pollution, land degradation, and deforestation. However, the semi-empirical technique used in water quality monitoring based on remote sensing imagery has some limitations. It has poor resolution, requires a large number of people for data collection, and has time and space constraints since it cannot analyze every picture from diverse imaging settings adaptively. Overall, the proposed technique for monitoring water quality using remote sensing data and artificial neural networks is a significant advancement in the field. It has the potential to provide accurate and reliable information about water quality, which is crucial for protecting the environment and public health.