A Safe Depth Forecasting Model for Insuring Tubewell Installations Against Arsenic Risk in Bangladesh
Nowadays large spatial databases are available to help analysts facing a variety of environmental risk problems. Statistically accurate and computationally efficient algorithms and models are then needed to extract knowledge from these, for inference and prediction of the studied phenomenon, and, ultimately for decision both at country-wide policy and local level. Arsenic concentrations are naturally elevated in groundwater pumped from millions of shallow tubewells distributed across rural Bangladesh. Deeper tubewells often make access to groundwater with lower arsenic levels. Thereby, also thanks to a relatively low installation cost, they have proven to be an effective method to reduce arsenic exposure. Relying on a large database of well tests conducted in thousands of villages, we propose a supervised learning technique to estimate the probability that a new well will be low in arsenic based on its location and depth. For villages lacking direct information to make a local prediction, our technique, that we call the Sister-Village method, combines data from villages with similar characteristics. To further promote safe well installations and to help disseminate the information resulting from our method, we also propose and price a simple insurance model.
KeywordsArsenic-depth pattern similarity Probability curve Bayesian learning Calibration plot Stratified cross-validation Probability score
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