Predicting sustainable arsenic mitigation using machine learning techniques.

This study evaluates state-of-the-art machine learning models in predicting the most sustainable arsenic mitigation preference. A Gaussian distribution-based Naïve Bayes (NB) classifier scored the highest Area Under the Curve (AUC) of the Receiver Operating Characteristic curve (0.82), followed by Nu Support Vector Classification (0.80), and K-Neighbors (0.79). Ensemble classifiers scored higher than 70% AUC, with Random Forest being the top performer (0.77), and Decision Tree model ranked fourth with an AUC of 0.77. The multilayer perceptron model also achieved high performance (AUC=0.75). Most linear classifiers underperformed, with the Ridge classifier at the top (AUC=0.73) and perceptron at the bottom (AUC=0.57). A Bernoulli distribution-based Naïve Bayes classifier was the poorest model (AUC=0.50). The Gaussian NB was also the most robust ML model with the slightest variation of Kappa score on training (0.58) and test data (0.64). The results suggest that nonlinear or ensemble classifiers could more accurately understand the complex relationships of socio-environmental data and help develop accurate and robust prediction models of sustainable arsenic mitigation. Furthermore, Gaussian NB is the best option when data is scarce

SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE

Toward an integrated disaster management approach: How artificial intelligence can boost disaster management

Technical and methodological enhancement of hazards and disaster research is identified as a critical question in disaster management. Artificial intelligence (AI) applications, such as tracking and mapping, geospatial analysis, remote sensing techniques, robotics, drone technology, machine learning, telecom and network services, accident and hot spot analysis, smart city urban planning, transportation planning, and environmental impact analysis, are the technological components of societal change, having significant implications for research on the societal response to hazards and disasters. Social science researchers have used various technologies and methods to examine hazards and disasters through disciplinary, multidisciplinary, and interdisciplinary lenses. They have employed both quantitative and qualitative data collection and data analysis strategies. This study provides an overview of the current applications of AI in disaster management during its four phases and how AI is vital to all disaster management phases, leading to a faster, more concise, equipped response. Integrating a geographic information system (GIS) and remote sensing (RS) into disaster management enables higher planning, analysis, situational awareness, and recovery operations. GIS and RS are commonly recognized as key support tools for disaster management. Visualization capabilities, satellite images, and artificial intelligence analysis can assist governments in making quick decisions after natural disasters

Groundwater research and management: integrating science into management decisions. Proceedings of IWMI-ITP-NIH International Workshop on "Creating Synergy Between Groundwater Research and Management in South and Southeast Asia," Roorkee, India, 8-9 February 2005

Groundwater management / Governance / Groundwater development / Artificial recharge / Water quality / Aquifers / Groundwater irrigation / Water balance / Simulation models / Watershed management / Water harvesting / Decision making / South East Asia / Bangladesh / China / India / Nepal / Pakistan / Syria

Comparative assessment of the vulnerability and resilience of 10 deltas : work document

Background information about: Nile delta (Egypt), Incomati delta (Mozambique), Ganges-Brahmaputra-Meghna (Bangladesh), Yangtze (China), Ciliwung (Indonesia), Mekong (Vietnam), Rhine-Meuse (The Netherlands), Danube (Romania), California Bay-Delta, Mississippi River Delta (USA

Admissible evidence in the court of development evaluation? : the impact of CARE's SHOUHARDO Project on child stunting in Bangladesh

Along with the rise of the development effectiveness movement of the last few decades, experimental impact evaluation methods – randomised controlled trials and quasiexperimental techniques – have emerged as a dominant force. While the increased use of these methods has contributed to improved understanding of what works and whether specific projects have been successful, their ‘gold standard’ status threatens to exclude a large body of evidence from the development effectiveness dialogue. In this paper we conduct an evaluation of the impact on child stunting of CARE’s SHOUHARDO project in Bangladesh, the first large-scale project to use the rights-based, livelihoods approach to address malnutrition. In line with calls for a more balanced view of what constitutes rigor and scientific evidence, and for the use of more diversified and holistic methods in impact evaluations, we employ a mixed-methods approach. The results from multiple data sources and methods, including both non-experimental and quasi-experimental, are triangulated to arrive at the conclusions. We find that the project had an extraordinarily large impact on stunting among children 6–24 months old – on the order of a 4.5 percentage point reduction per year. We demonstrate that one reason the project reduced stunting by so much was because, consistent with the rights-based, livelihoods approach, it relied on both direct nutrition interventions and those that addressed underlying structural causes including poor sanitation, poverty, and deeply-entrenched inequalities in power between women and men. These findings have important policy implications given the slow progress in reducing malnutrition globally and that the widely-supported Scaling Up Nutrition initiative aimed at stepping up efforts to do so is in urgent need of guidance on how to integrate structural cause interventions with the direct nutrition interventions that are the initiative’s main focus. The evaluation also adds to the evidence that targeting the poor, rather than employing universal coverage, can help to accelerate reductions in child malnutrition. The paper concludes that, given the valuable policy lessons generated, the experience of the SHOUHARDO project merits solid standing in the knowledge bank of development effectiveness. More broadly, it illustrates how rigorous and informative evaluation of complex, multi-intervention projects can be undertaken even in the absence of the randomisation, nonproject control groups and/or panel data required by the experimental methods. Keywords: development effectiveness; impact evaluation; experimental methods; child malnutrition; Bangladesh

Internet of Things for Sustainable Human Health

The sustainable health IoT has the strong potential to bring tremendous improvements in human health and well-being through sensing, and monitoring of health impacts across the whole spectrum of climate change. The sustainable health IoT enables development of a systems approach in the area of human health and ecosystem. It allows integration of broader health sub-areas in a bigger archetype for improving sustainability in health in the realm of social, economic, and environmental sectors. This integration provides a powerful health IoT framework for sustainable health and community goals in the wake of changing climate. In this chapter, a detailed description of climate-related health impacts on human health is provided. The sensing, communications, and monitoring technologies are discussed. The impact of key environmental and human health factors on the development of new IoT technologies also analyzed