Authors: Sarigai Davshilt*, Eastern Michigan University
Topics: Water Resources and Hydrology, Remote Sensing, Quantitative Methods
Keywords: Urban Black and Odorous Water, Remote Sensing, Machine Learning, Hypersepctral
Session Type: Lightning Paper
Start / End Time: 5:35 PM / 6:50 PM
Room: Governors Square 16, Sheraton, Concourse Level
Presentation File: No File Uploaded
In recent decades, with the significant and expeditious development of society and economy, different types of pollution to the living space of human beings and even the environment which is caused mainly by industry wastewater and urban domestic sewage have been attracting public and policymakers’ attention. Among various pollutions, water pollution became one of the most considerable issues for human development and sustainability due to its tremendous hazard to human health and environmental health. Developing countries are most affected by water quality deterioration because of its rapid urbanization. When water is polluted to be black and produces a smelly gas, generally, it called a black and odorous water phenomenon. It is one of the most hazardous issues among various environmental pollution since it not only has an extremely bad influence on human development and sustainability but also is a big threat to the survival of human beings.
Current field-based methods for detecting black and odorous water are costly and time-consuming. Fortunately, the extremely narrow wavebands became the advantage of hyperspectral remote sensing to study urban black odorous water phenomena, on the other hand, it also increases the difficulty of selecting the optimal bands of different water quality indicators due to its detailed reflectance bands and huge dataset. In this paper, I discussed the implementation of remote sensing techniques and machine learning techniques to the study of urban black and odorous water pollution under the supporting of in-situ data, moreover, to overcome the above-mentioned difficulties.