Prediction of water quality for lake monitoring system using machine learning approach

• Main Author: Hong, Yuen Mun
• Format: Academic Exercise
• Language: English; English
• Published: 2022
• Subjects: Q1-390 Science (General); QH1-278.5 Natural history (General)
• Online Access: https://eprints.ums.edu.my/id/eprint/33294/1/Prediction%20of%20Water%20Quality%20for%20Lake%20Monitoring%20System%20using%20Machine%20Learning%20Approach.24pages.pdf; https://eprints.ums.edu.my/id/eprint/33294/2/Prediction%20of%20Water%20Quality%20for%20Lake%20Monitoring%20System%20using%20Machine%20Learning%20Approach.pdf; https://eprints.ums.edu.my/id/eprint/33294/

Water quality in lakes is a critical issue due to its direct influence on public health, biological integrity of natural resources, and the economy. There are a variety of lakes, from small to big, natural and manmade reservoirs to natural lakes. Even though the lakes only consist small part of water on our planet, they play an important role in earth’s biosphere, climate changes, land-use changes, and anthropogenic changes due to various urban and industrial development can lead to hydrological, chemical, and biological changes in watersheds and freshwater ecosystems resulting in altered water quality. The deteriorating quality of natural water resources like lakes is one of the dire problems and most concerning issues faced by humanity. Lack of quality lake water is most likely because lake water became contaminated due to various factors such as humans, industrial, commercial activities, and natural processes. To understand the impact of changes from upstream or surrounding watersheds and within a lake on water quality is important to people who live nearby or visit the lake and is also fundamental in providing better ecological and environmental strategies and mitigation methods to protect the freshwater ecosystems. Dissolved oxygen and other water quality will affect the growth, reproduction, and survivability of freshwater organisms. Climate variations can directly affect the temperature of an aquatic system through the surface heat exchange between the water and the surrounding atmosphere and further influence water quality characteristics. Monitoring and modeling approaches have been used by volunteers, biologists, water resources managers, engineers, and scientists to understand and further study water quality issues in the lake. Therefore, the monitoring and prediction of lake water quality will provide more in-depth and necessary information and evidence to help in managing the water quality. Monitoring data are necessary for model calibration and validation before the model can be used for scenario study, sensitive analysis, and future projection under certain changes in lakes water. In this project, a machine learning approach is proposed to assist the prediction of the lake water quality. The evaluation will then be used to predict the quality of water.