https://hal-audencia.archives-ouvertes.fr/hal-01996486/document

Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain

The era of big data and analytics is opening up new possibilities for disaster management (DM). Due to its ability to visualize, analyze and predict disasters, big data is changing the humanitarian operations and crisis management dramatically. Yet, the relevant literature is diverse and fragmented, which calls for its review in order to ascertain its development. A number of publications have dealt with the subject of big data and its applications for minimizing disasters. Based on a systematic literature review, this study examines big data in DM to present main contributions, gaps, challenges and future research agenda. The study presents the findings in terms of yearly distribution, main journals, and most cited papers. The findings also show a classification of publications, an analysis of the trends and the impact of published research in the DM context. Overall the study contributes to a better understanding of the importance of big data in disaster management.

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Big data and disaster management: a systematic review and agenda for future research

Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers

The field of supply chain management has been extensively studied, while its role in disaster relief operations has received limited contributions. Recent dedicated special issues on Humanitarian Operations and Supply Chain Management (HOSCM) and a dedicated Journal focusing on humanitarian logistics and supply chain management clearly indicate the growing popularity of the HOSCM literature. The purpose of our current study is to undertake an extensive review of extant literature published in operations and supply chain management journals as well as popular interdisciplinary journals. A review of 362 papers published between 2011 and 2017 provides a thematic outline of the study. The study pivots around nine key themes, which have gained prominent attention from HOSCM scholars, and draws a roadmap from reviewing earlier review papers to performance evaluation of HOSCM related studies. Some of the key themes include humanitarian logistics, theory focused research, case studies, mathematical models, humanitarian supply chain properties and resources needed for efficient and effective management of humanitarian operations. Finally, our study offers several research directions which may take the existing debates to a next level.

Humanitarian supply chain management: a thematic literature review and future directions of research

The aim of the current study was to suggest a multi-hazard probability assessment in Fars Province, Shiraz City, and its four strategic watersheds. At first, we construct maps depicting the most effective factors on floods (12 factors), forest fires (10 factors), and landslides (10 factors), and used the Boruta algorithm to prioritize the impact of each respective factor on the occurrence of each hazard. Subsequently, flood, landslides, and forest fire susceptibility maps prepared using a Random Forest (RF) model in the R statistical software. Results indicate that 42.83% of the study area are not susceptible to any hazards, while 2.67% of the area is at risk of all three hazards. The results of the multi-hazard map in Shiraz City indicate that 25% of Shiraz city is very susceptible to flooding, while 16% is very susceptible to landslide occurrences. For four strategic watersheds, it is notable that in the Dorodzan Watershed, landslides and floods are the most important hazards; whereas, flood occurrences cover the largest area of the Maharlou Watershed. In contrast, the Tashk-Bakhtegan Watershed is so sensible to floods and landslides, respectively. Finally, in the Ghareaghaj Watershed, forest fire ranks as the strongest hazard, followed by floods. The validation results indicate an AUC of 0.834, 0.939, and 0.943 for the flood, landslide, and forest fire susceptibility maps, respectively. Also, other accuracy measures including, specificity, sensitivity, TSS, CCI, and Gini coefficient confirmed results of the AUC values. These results allow us to forecast the spatial behavior of such multi-hazard events, and researchers and stakeholders alike can apply them to evaluate hazards under various mitigation scenarios.

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Assessing and mapping multi-hazard risk susceptibility using a machine learning technique.

A review on application of data mining techniques to combat natural disasters

A large discrepancy between simulated and observed black carbon (BC) surface concentrations over the densely populated Indo-Gangetic plain (IGP) has so far limited our ability to assess the magnitude of BC health impacts in terms of population exposure, morbidity, and mortality. We evaluate these impacts using an integrated modeling framework, including successfully predicted BC concentrations. Population exposure to BC is notable, with more than 60 million people identified as living in hotspots of BC concentration (wintertime mean, >20 μg m−3). The attributable fraction of the total cardiovascular disease mortality (CVM) burden to BC exposures is 62% for the megacity. The semiurban area comprised about 49% of the total BC-attributable CVM burden over the IGP. More than 400,000 lives can potentially be saved from CVM annually by implementing prioritized emission reduction from the combustion of domestic biofuel in the semiurban area, diesel oil in transportation, and coal in thermal power plant and brick kiln industries in megacities.

Black carbon health impacts in the Indo-Gangetic plain: Exposures, risks, and mitigation

The ever-increasing urbanization to accommodate the growing population reduces substantially the agricultural land but poses a threat to meeting the requirement of proper nutrition for human health. Mungbean [Vigna radiata (L.) Wilczek] is a unique gift bestowed by nature to mankind, which has the potency to make up the gap of protein shortage with an inexpensive cost, but due to its low level of production as well as productivity, which in a roundabout way influences the nutritional status of people resulting in malnutrition. Therefore, enhancement of the total area under mungbean cultivation is not permissible, and an increase in the total productivity per unit area is necessary. In this manner, screening and evaluation of improved genotypes for high yield are necessary to ensure food security. But at the same time seed yield being a complex character governed by several other contributing traits, selection for the characters proves to be quite challenging. As a prerequisite for any breeding program aimed at yield enhancement presence of significant genetic diversity in a given population is highly important. In the present investigation principal component analysis was performed and the results revealed two principal components contributing to the total variance in the population. While the PC1 was predominated by yield and its attributing traits, the PC2 was mainly comprised of growth-related traits. The hierarchical (UPGMA) cluster analysis using standardized data classified the fifty-two mungbean genotypes into 4 clusters, which showed 2 major, 1 minor and one outlier. Among them, cluster II is the most fascinating, as its individual had high seed yield plant–1 and related traits. The present work concluded that the identification of promising high-yielding mungbean genotypes through multivariate analysis has a good promise for future breeding programs with a view of food and nutritional security.

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Screening of Elite Mungbean Genotypes (Vigna radiata (L.) Wilczek) through Multivariate Analysis for Food and Nutritional Security

Estimates of Spatially and Temporally Resolved Constrained Organic Matter and Sulfur Dioxide Emissions Over the Indian Region Through the Strategic Source Constraints Modelling

Thousands of human lives are lost every year around the globe, apart from significant damage on property, animal life, etc., due to natural disasters (e.g., earthquake, flood, tsunami, hurricane and other storms, landslides, cloudburst, heat wave, forest fire). In this paper, we focus on reviewing the application of data mining and analytical techniques designed so far for (i) prediction, (ii) detection, and (iii) development of appropriate disaster management strategy based on the collected data from disasters. A detailed description of availability of data from geological observatories (seismological, hydrological), satellites, remote sensing and newer sources like social networking sites as twitter is presented. An extensive and in-depth literature study on current techniques for disaster prediction, detection and management has been done and the results are summarized according to various types of disasters. Finally a framework for building a disaster management database for India hosted on open source Big Data platform like Hadoop in a phased manner has been proposed. The study has special focus on India which ranks among top five counties in terms of absolute number of the loss of human life.

Sanhita Ghosh

Papers

A review on application of data mining techniques to combat natural disasters

Black carbon health impacts in the Indo-Gangetic plain: Exposures, risks, and mitigation

Screening of Elite Mungbean Genotypes (Vigna radiata (L.) Wilczek) through Multivariate Analysis for Food and Nutritional Security

Estimates of Spatially and Temporally Resolved Constrained Organic Matter and Sulfur Dioxide Emissions Over the Indian Region Through the Strategic Source Constraints Modelling

A review on application of data mining techniques to combat natural disasters