Combining Multi-Modal Statistics for Welfare Prediction Using Deep Learning

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.

Fine-Grained Large-Scale Vulnerable Communities Mapping via Satellite Imagery and Population Census Using Deep Learning

“…Our approach to the definition of vulnerability aims to find a tradeoff between the terms proposed by UN-Habitat [27], the data publicly available for its countrywide detection [30], and an agnostic approach about the relative importance of the factors employed. Other sources of information, e.g., whether houses receive electricity, or prior knowledge of the importance of the different reference values may provide to be useful in a more refined labeling associated with the images and further performance of the automatic inference mechanisms, as shown by Sharma et al [24] and Ibrahim et al [23]. Establishing a baseline of comparison with other classical [8,12,16,22] or modern approaches [13,14,21,23,24] is most challenging.…”

“…Other sources of information, e.g., whether houses receive electricity, or prior knowledge of the importance of the different reference values may provide to be useful in a more refined labeling associated with the images and further performance of the automatic inference mechanisms, as shown by Sharma et al [24] and Ibrahim et al [23]. Establishing a baseline of comparison with other classical [8,12,16,22] or modern approaches [13,14,21,23,24] is most challenging. For one, the data sources, dense [8] or sparse [13], may have local and unique components; the scope may be a citywide [18], countrywide [26], or regionwide [15,25] interest.…”

“…Both high-resolution [10,12,14] and low-resolution [10,11,[15][16][17] images have been considered in rural [9] and urban areas in multispectral [9,10,17], Synthetic Aperture Radar (SAR) [11,13], and color [19,21] images. Multiple machine learning methods including traditional [8,12,16,18,22] approaches such as Random Forest (RF), gradient boosting, Support Vector Machines (SVM), and modern techniques [11,13,14,19,21,23,24] such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been deployed. Some open-source code has been made available to the research community [25].…”