A Multivariate Time Series and Machine Learning Approach for Predicting Groundwater Arsenic Variation

Author:

Wang Sheng-Wei,Liang Ching-Ping,Kao Yu-Hsuan,Chung Chia-Ru,Wu Li-Cheng,Horng Jorng-Tzong,Suk Heejun,Chen Jui-Sheng

Publisher

Elsevier BV

Reference39 articles.

1. Building and Applying Logistic Regression Models;A Agresti;Categorical Data Analysis,2003

2. Arsenic in Groundwater: A Summary of Sources and the Biogeochemical and Hydrogeologic Factors Affecting Arsenic Occurrence and Mobility;J L Barringer;Current Perspectives in Contaminant Hydrology and Water Resources Sustainability. IntechOpen,2013

3. Effects of Seasonal Operation on the Quality of Water Produced by Public-Supply Wells;L M Bexfield;Groundwater,2014

4. Non-renewable groundwater use and groundwater depletion: a review;M F P Bierkens;Environ. Res. Lett,2019

5. Predicting groundwater arsenic contamination: Regions at risk in highest populated state of India;S Bindal;Water Research,2019

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