Research Article
Applying Geostatistics to Predict Dissolvent Oxygen (DO) in Water on the Rivers in Ho Chi Minh City
@INPROCEEDINGS{10.1007/978-3-030-34365-1_20, author={Cong Nguyen}, title={Applying Geostatistics to Predict Dissolvent Oxygen (DO) in Water on the Rivers in Ho Chi Minh City}, proceedings={Context-Aware Systems and Applications, and Nature of Computation and Communication. 8th EAI International Conference, ICCASA 2019, and 5th EAI International Conference, ICTCC 2019, My Tho City, Vietnam, November 28-29, 2019, Proceedings}, proceedings_a={ICCASA \& ICTCC}, year={2019}, month={12}, keywords={Geostatistics Interpolation Kriging Spatial Variogram}, doi={10.1007/978-3-030-34365-1_20} }- Cong Nguyen
Year: 2019
Applying Geostatistics to Predict Dissolvent Oxygen (DO) in Water on the Rivers in Ho Chi Minh City
ICCASA & ICTCC
Springer
DOI: 10.1007/978-3-030-34365-1_20
Abstract
Geostatistics is briefly concerned with estimation and prediction for spatially continuous phenomena, using data measured at a finite number of spatial locations to estimate values of interest at unmeasured locations. In practice, the costs of installing new observational stations to observe metropolitan water pollution sources, as DO (Dissolvent Oxygen), COD (Chemical Oxygen Demand) and BOD (Biochemical oxygen Demand) concentrations are economically high. In this study, spatial analysis of water pollution of 32 stations monitored during 3 years was carried out. Geostatistics which has been introduced as a management and decision tool by many researchers has been applied to reveal the spatial structure of water pollution fluctuation. In this article, author use the recorded DO concentrations (is the amount of dissolvent oxygen in water required for the respiration of aquatic organisms) at several observational stations on the rivers in Ho Chi Minh City (HCMC), employ the Kriging interpolation method to find suitable models, then predict DO concentrations at some unmeasured stations in the city. Our key contribution is finding good statistical models by several criteria, then fitting those models with high precision. From the data set, author found the best forecast model with the smallest forecast error to predict DO concentration on rivers in Ho Chi Minh City. From there we propose to the authorities to improve areas where DO concentrations exceed permissible levels.
