A new forecasting model for groundwater quality based on short time series monitoring data

3rd International Conference on Energy Engineering and Environmental Protection

能源学;生态环境科学

Yao, Ling^1^2^3 ; Zhu, Yunqiang^1^2

Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing

100101, China^1

Jiangsu Ctr. for Collab. Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing

210023, China^2

Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou

510070, China^3

关键词: Expectation-maximization algorithms;    Forecasting modeling;    Moving average model;    Rapid urban development;    Regional groundwater;    Regional water resources;    Short time series;    Surface precipitation;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/227/6/062014/pdf
DOI  :  10.1088/1755-1315/227/6/062014

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Groundwater is an important part of regional water resource, rapid urban development often witness deterioration of regional groundwater quality. This paper proposed a missing-aware-weighted hidden markov model (MWMO-HMM) combining expectation maximization algorithm (EM) with a weighted multi-order HMM to build groundwater quality prediction model with incomplete short-term observations. The proposed model was used to predict hydrogen ion concentration (PH) and chemical oxygen demand (COD) of groundwater in five representative cities. The Nash-Sutcliffe model efficiency coefficients of MWMO-HMM prediction results are respectively 61.51% and 98.06%. Compared with prediction results achieved by auto-regressive and moving average model (ARMA) and gray model (GM), the results show that MWMO-HMM is superior to ARMA and GM, ARMA and GM demonstrate an unstable performance of forecasting. In addition, missing value has a greater effect on ARMA than GM. Furthermore, the integral observations filled with EM algorithm indicates that COD concentration of karst groundwater in Guizhou is affected to some extent by the surface precipitation. The proposed model can predict groundwater quality effectively and meet the management requirements in groundwater prediction based on disintegrated small sample datasets. It would assist decision makers to enhance the decision making for future sustainable development.