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 levels 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, thanks also 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.

A Safe Depth Forecasting Model for Insuring Tubewell Installations Against Arsenic Risk in Bangladesh

TREVISANI, MATILDE
;
2017

Abstract

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 levels 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, thanks also 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.
978-3-319-62403-7
978-3-319-62404-4
File in questo prodotto:
File Dimensione Formato  
Trevisani_A Safe Depth Forecasting Model for Insuring Tubewell Installations Against Arsenic Risk in Bangladesh.pdf

non disponibili

Tipologia: Documento in Versione Editoriale
Licenza: Digital Rights Management non definito
Dimensione 4.38 MB
Formato Adobe PDF
4.38 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
2903936_Trevisani_A Safe Depth Forecasting Model for Insuring Tubewell Installations Against Arsenic Risk in Bangladesh-PostPrint.pdf

accesso aperto

Descrizione: PostPrint VQR3
Tipologia: Bozza finale post-referaggio (post-print)
Licenza: Digital Rights Management non definito
Dimensione 4.29 MB
Formato Adobe PDF
4.29 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11368/2903936
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact