Combining Multi-Modal Statistics for Welfare Prediction Using Deep Learning

Pulkit Sharma, Achut Manandhar, Patrick Thomson, Jacob Katuva, Robert Hope and David A. Clifton
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Pulkit Sharma: Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
Achut Manandhar: Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
Patrick Thomson: School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK
Jacob Katuva: School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK
Robert Hope: School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK
David A. Clifton: Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK

Sustainability, 2019, vol. 11, issue 22, 1-15

Abstract: In the context of developing countries, effective groundwater resource management is often hindered by a lack of data integration between resource availability, water demand, and the welfare of water users. As a consequence, drinking water-related policies and investments, while broadly beneficial, are unlikely to be able to target the most in need. To find the households in need, we need to estimate their welfare status first. However, the current practices for estimating welfare need a detailed questionnaire in the form of a survey which is time-consuming and resource-intensive. In this work, we propose an alternate solution to this problem by performing a small set of cost-effective household surveys, which can be collected over a short amount of time. We try to compensate for the loss of information by using other modalities of data. By combining different modalities of data, this work aims to characterize the welfare status of people with respect to their local drinking water resource. This work employs deep learning-based methods to model welfare using multi-modal data from household surveys, community handpump abstraction, and groundwater levels. We employ a multi-input multi-output deep learning framework, where different types of deep learning models are used for different modalities of data. Experimental results in this work have demonstrated that the multi-modal data in the form of a small set of survey questions, handpump abstraction data, and groundwater level can be used to estimate the welfare status of households. In addition, the results show that different modalities of data have complementary information, which, when combined, improves the overall performance of our ability to predict welfare.

Keywords: deep learning; data fusion; groundwater risk management; Kenya; welfare; rural water supply (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
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