Developing robust arsenic awareness prediction models using machine learning algorithms

• DOI: 10.1016/j.jenvman.2018.01.044
• Publication Date: 2018-02-04T14:40:31Z
• Authors: Sushant K. Singh; Robert W. Taylor; Mohammad Mahmudur...; Biswajeet Pradhan
• Keywords: machine learning alorithms; Support Vector Machine; arsenic; Sustainable Development Goals; 1. No poverty; India; sociobehavioral; Bayes Theorem; 6. Clean water; Arsenic; 3. Good health; socioeconomic; Machine Learning; 03 medical and health sciences; 0302 clinical medicine; demographics; 616; 11. Sustainability; Humans; awareness; support vector machine (SVM); Environmental Pollutants; SDG 6; Algorithms

Arsenic awareness plays a vital role in ensuring the sustainability of arsenic mitigation technologies. Thus far, however, few studies have dealt with the sustainability of such technologies and its associated socioeconomic dimensions. As a result, arsenic awareness prediction has not yet been fully conceptualized. Accordingly, this study evaluated arsenic awareness among arsenic-affected communities in rural India, using a structured questionnaire to record socioeconomic, demographic, and other sociobehavioral factors with an eye to assessing their association with and influence on arsenic awareness. First a logistic regression model was applied and its results compared with those produced by six state-of-the-art machine-learning algorithms (Support Vector Machine [SVM], Kernel-SVM, Decision Tree [DT], k-Nearest Neighbor [k-NN], Naïve Bayes [NB], and Random Forests [RF]) as measured by their accuracy at predicting arsenic awareness. Most (63%) of the surveyed population was found to be arsenic-aware. Significant arsenic awareness predictors were divided into three types: (1) socioeconomic factors: caste, education level, and occupation; (2) water and sanitation behavior factors: number of family members involved in water collection, distance traveled and time spent for water collection, places for defecation, and materials used for handwashing after defecation; and (3) social capital and trust factors: presence of anganwadi and people's trust in other community members, NGOs, and private agencies. Moreover, individuals' having higher social network positively contributed to arsenic awareness in the communities. Results indicated that both the SVM and the RF algorithms outperformed at overall prediction of arsenic awareness-a nonlinear classification problem. Lower-caste, less educated, and unemployed members of the population were found to be the most vulnerable, requiring immediate arsenic mitigation. To this end, local social institutions and NGOs could play a crucial role in arsenic awareness and outreach programs. Use of SVM or RF or a combination of the two, together with use of a larger sample size, could enhance the accuracy of arsenic awareness prediction.