Special Issue "Internet of Things, Remote Sensing and Analytics to Support Distributed Monitoring and Management of Water, Sanitation, Agricultural and Energy Resources in Remote and Low Income Regions"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Use of the Environment and Resources".

Deadline for manuscript submissions: 29 February 2020.

Special Issue Editor

Dr. Evan Thomas
E-Mail Website
Guest Editor
Mortenson Center in Global Engineering, University of Colorado Boulder, Boulder, CO 80309, USA
Interests: global engineering; global health; water; sanitation; agriculture; energy; ICT4D; remote sensing

Special Issue Information

Dear Colleagues,

Monitoring and managing distributed water, sanitation, agricultural, and energy resources and services in remote and/or low-income regions are increasingly important as population pressures and climate change impact the reliability of these resources. The aim and scope of this Special Issue of Sustainability is to present and review emerging methods and technologies including “internet of things” sensor systems, cellular-based data collection, remote sensing, machine learning, and other analytical tools designed to support the remote monitoring and management of water, sanitation, agricultural, and energy resources in remote and/or low-income regions. Examples may include remotely reporting sensor technologies for monitoring water service infrastructure; satellite-based remote sensing of agricultural yields; localized air quality monitoring; cellular-based survey and decision support tools; and machine learning-enabled analytics. Papers selected for this Special Issue will be subject to a rigorous peer review procedure with the aim of rapid and wide dissemination of research results, developments, and applications.

Assoc. Prof. Dr. Evan Thomas
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • IOT
  • Sensors
  • Remote sensing
  • Global health
  • ICT4D
  • Water
  • Sanitation
  • Agriculture
  • Energy

Published Papers (4 papers)

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Research

Open AccessArticle
Combining Multi-Modal Statistics for Welfare Prediction Using Deep Learning
Sustainability 2019, 11(22), 6312; https://doi.org/10.3390/su11226312 - 11 Nov 2019
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 [...] Read more.
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. Full article
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Open AccessArticle
Changes in Land Cover in Cacheu River Mangroves Natural Park, Guinea-Bissau: The Need for a More Sustainable Management
Sustainability 2019, 11(22), 6247; https://doi.org/10.3390/su11226247 - 07 Nov 2019
Abstract
The aim of this paper is to study the evolution of vegetation and potential changes in land use in the Cacheu River Mangroves Natural Park in the Republic of Guinea-Bissau. To do this, we will study variations in the Normalized Difference Vegetation Index [...] Read more.
The aim of this paper is to study the evolution of vegetation and potential changes in land use in the Cacheu River Mangroves Natural Park in the Republic of Guinea-Bissau. To do this, we will study variations in the Normalized Difference Vegetation Index (NDVI). In order to perform the calculations and subsequent analysis, images of the park from the years 2010 and 2017, corresponding to the same period of the year, so that the phenological stage is the same, were used. To perform a more reliable analysis, the park was divided into five different areas based upon the vegetation type or main use of the land in each of them; i.e.: mangals, palm forest, paddies, savannahs and others. Using a statistical sample, the NDVIs were calculated for each of these areas. The study made it possible to conclude that the changes in land cover observed represent a decrease in mangrove swamps, which are probably being replaced by other land uses, despite the fact that these forests constitute the most important ecological area of all those that make up the park. The park will therefore benefit from a more sustainable management. Full article
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Open AccessArticle
Improved Drought Resilience Through Continuous Water Service Monitoring and Specialized Institutions—A Longitudinal Analysis of Water Service Delivery Across Motorized Boreholes in Northern Kenya
Sustainability 2019, 11(11), 3046; https://doi.org/10.3390/su11113046 - 29 May 2019
Abstract
Increasing frequency and severity of drought is driving increased use of groundwater resources in arid regions of Northern Kenya, where approximately 2.5 million people depend on groundwater for personal use, livestock, and limited irrigation. As part of a broader effort to provide more [...] Read more.
Increasing frequency and severity of drought is driving increased use of groundwater resources in arid regions of Northern Kenya, where approximately 2.5 million people depend on groundwater for personal use, livestock, and limited irrigation. As part of a broader effort to provide more sustainable water, sanitation, and hygiene services in the region, we have collected data related to site functionality and use for approximately 120 motorized boreholes across five counties. Using a multilevel model to account for geospatial and temporal clustering, we found that borehole sites, which counties had identified as strategic assets during drought, ran on average about 1.31 h less per day compared to non-strategic borehole sites. As this finding was contrary to our hypothesis that strategic boreholes would exhibit greater use on average compared to non-strategic boreholes, we consider possible explanations for this discrepancy. We also use a coupled human and natural systems framework to explore how policies and program activities in a complex system depend on consistent and reliable feedback mechanisms. Funding was provided by the United States Agency for International Development. The views expressed in this article do not necessarily reflect the views of the United States Agency for International Development or the United States Government. Full article
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Open AccessArticle
An Efficient Burst Detection and Isolation Monitoring System for Water Distribution Networks Using Multivariate Statistical Techniques
Sustainability 2019, 11(10), 2970; https://doi.org/10.3390/su11102970 - 24 May 2019
Abstract
Detection and isolation of burst locations in water distribution networks (WDN) are challenging problems in urban management because burst events cause considerable economic, social, and environmental losses. In the present study, a novel monitoring and sensor placement approach is proposed for rapid and [...] Read more.
Detection and isolation of burst locations in water distribution networks (WDN) are challenging problems in urban management because burst events cause considerable economic, social, and environmental losses. In the present study, a novel monitoring and sensor placement approach is proposed for rapid and robust burst detection. Accordingly, a hybrid principal component analysis (PCA) and standardized exponential weighted moving average (EWMA) system is proposed for WDN monitoring and management. In addition, the optimal sensor configuration is obtained using PCA, k-means clustering, and a sensitivity analysis considering the diurnal patterns and the noises of pressure and flowrate data in the WDN. The proposed system is applied to a branched WDN, and the results are compared to those obtained with conventional monitoring systems. The results show that the proposed system detected the burst occurrence regardless of noise size with a detection rate of 93%. Compared to conventional systems, the isolation ratio improved by 10%, indicating that the bursts were isolated more accurately. In addition, the corresponding sensor configuration was 40% less expensive than the conventional systems. Full article
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