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.
Highlights
Sustainability is a multidimensional and dynamic problem where the most severe challenges often exist in the places with the least data
Compared to prior deep learning-based studies, the proposed work differs in two key ways: (1) with a primary motivation to generate data and tools required to inform policies, interventions and investments related to drinking water management, we focus on predicting the welfare status of people with respect to drinking water resource, and (2) our model combines household level socio-economic survey data with two other modalities of data such that the welfare can be modeled with respect to their proximal drinking water resource
We have demonstrated that a small set of survey questions along with groundwater level and handpump abstraction data can be used to predict the welfare status of households
Summary
Sustainability is a multidimensional and dynamic problem where the most severe challenges often exist in the places with the least data. Despite recent global push to ramp up data collection within developing nations [3], the use of traditional household surveys alone to close these gaps may not be cost-effective—it may require billions of US dollars to meet the United Nations sustainable development goals target [8]. To mitigate this problem, researchers have employed alternate methods to measure these outcomes using data from search engines [9], social networks [10], or mobile phone networks [11]
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