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Predictive Modeling through Earth Observational Data Analysis for Natural Hazards...
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Predictive Modeling through Earth Observational Data Analysis for Natural Hazards Risk Assessment and Disaster Management

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Hazards and Sustainability".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 4001

Special Issue Editors

Prof. Dr. Arvind Chandra Pandey

E-Mail Website
Guest Editor
Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, Brambe, Ranchi 835205, Jharkhand, India
Interests: natural hazard risk assessment (flood, landslides, drought, forest fire, avalanche-permafrost destabilisation); cryospheric studies; coastal hazards; water resources management; forest mapping and health analysis; urban environment; geoinformatics
Dr. Bikash Ranjan Parida

E-Mail Website
Guest Editor
Department of Geoinformatics, School of Natural Resources Management, Central University of Jharkhand, Ranchi, India
Interests: vegetation remote sensing; disaster and hazard risk analysis; natural resources management; climate Modelling and climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Surajit Ghosh

E-Mail Website
Guest Editor
International Water Management Institute (IWMI), Colombo 10120, Sri Lanka
Interests: disaster risks; flood and drought; decision support system; earth observation; big data analytics; cloud computing; biophysical modelling

Special Issue Information

Dear Colleagues,

We are pleased to introduce a new Special Issue focusing on “Predictive Modeling through Earth Observational Data Analysis for Natural Hazards Risk Assessment and Disaster Management”. The natural geo-environment of planet Earth is under extreme stress caused largely by human-induced dynamic perturbation in the natural environment and climate domain. Impact assessment of climate change on the natural setup of planet Earth observed through the increasing intensity of natural hazards is vital for understanding the future potential and risk to humans, as well as devising techniques to combat the ill effects of natural hazards and human-induced disasters.

Earth observation (EO) data obtained via remote sensing provide adequate opportunities to model the geo-aspects of different hazard potentials and their risk-inducing capabilities. When modeled through geoinformatics techniques, such spatial data become very effective in near real-time hazard predictions. Such cost-effective solutions can safeguard human fatalities by spatial planning during natural-anthropogenic disasters. 

Global warming and climate change impacts are felt globally with disastrous consequences during floods, avalanches, landslides, forest fires, drought, and cyclones, among others. The localised or global dynamics of water–air interaction cause changing patterns of rainfall and temperature that are responsible for inducing the majority of natural hazards. The fast-growing urban habitat and concomitant land use changes caused by increasing population and mass migration from rural to urban areas, coupled with shrinking forests across the globe and sea-level rise induced by glacier melting due to global warming, are impeding such geo-environmental changes and the resulting hazards can be monitored using EO. Hence, the geospatial modeling under different platforms will effectively lead to managing natural and human hazards. The present Special Issue aims to reconcile multi-disciplinary scientific knowledge of EO and climate data for mapping natural hazards and human-induced disasters using geospatial modelling approaches.

The list of possible topics of interest includes:

  • Impacts of urban–rural land use and land cover change on environmental sustainability
  • Climate change-induced impacts on water resources
  • Rainfall intensity and landsides hazards in mountainous regions
  • Hydrological modeling for water-induced hazards
  • Cryosphere permafrost-induced hazards
  • Glacier retreat, snow cover changes, avalanches, GLOF
  • Forest health–climate interactions and forest fires
  • Mangrove forest–sea level rise interaction and coastal erosion
  • Global warming impacts on cyclonic storms
  • Urban land use land cover changes, microclimatic variability and hazards
  • Natural hazard modelling approaches—opportunities and constraints
  • Environmental pollution impacts and predictive modeling

Prof. Dr. Arvind Chandra Pandey
Dr. Bikash Ranjan Parida
Dr. Surajit Ghosh
Guest Editors

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 submissions that pass pre-check are 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 2400 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

  • land use and land cover dynamics
  • environmental sustainability
  • forest eco-system
  • climate change
  • land-sides hazards
  • cryosphere permafrost
  • coastal erosion
  • global warming
  • cyclones
  • environmental pollution impacts and predictive modeling

Published Papers (2 papers)

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Research

25 pages, 6053 KiB  
Article
A Comparative Study of Genetic Algorithm-Based Ensemble Models and Knowledge-Based Models for Wildfire Susceptibility Mapping
by Abdel Rahman Al-Shabeeb, Ibraheem Hamdan, Sedigheh Meimandi Parizi, A’kif Al-Fugara, Sana’a Odat, Ismail Elkhrachy, Tongxin Hu and Saad Sh. Sammen
Sustainability 2023, 15(21), 15598; https://doi.org/10.3390/su152115598 - 3 Nov 2023
Cited by 1 | Viewed by 764
Abstract
Wildfire susceptibility mapping (WSM) plays a crucial role in identifying areas with heightened vulnerability to forest fires, allowing for proactive measures in fire prevention, management, and resource allocation, ultimately leading to more effective fire control and mitigation strategies. This paper describes our undertaking [...] Read more.
Wildfire susceptibility mapping (WSM) plays a crucial role in identifying areas with heightened vulnerability to forest fires, allowing for proactive measures in fire prevention, management, and resource allocation, ultimately leading to more effective fire control and mitigation strategies. This paper describes our undertaking to develop and compare the performance of two knowledge-based models, namely the analytic hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS), as well as two novel genetic algorithm (GA)-based ensemble data-driven models: boosting and random subspace. The objective was to map susceptibility to forest fires in the Northern Mazar District in Jordan. The ensemble models were constructed using four well-known classifiers: decision tree (DT), support vector machine (SVM), k-nearest neighbors (kNN), and naive Bayes (NB) algorithms. This study utilized seventy forest fire locations and twelve influential factors to build and evaluate the models. To identify the optimal features for constructing the data-driven models, a GA-based wrapper method and four machine learning models were applied. During the validation phase, the area under the receiver operating characteristic curve (AUROCC) values for the single SVM, single NB, single DT, single kNN, GA-based boosting, GA-based random subspace, FR-AHP, and AHP-TOPSIS models were found to be 85.3%, 85.9%, 73.8%, 88.7%, 95.0%, 95.0%, 74.0%, and 65.4% respectively. The results indicated that the GA-based ensemble models outperformed both the single machine learning models and the knowledge-based techniques in terms of performance. The developed models in this study can be effectively utilized in various management and decision-making processes aimed at mitigating forest fire risks and enhancing fire control strategies. Full article
(This article belongs to the Special Issue Predictive Modeling through Earth Observational Data Analysis for Natural Hazards Risk Assessment and Disaster Management)
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19 pages, 46573 KiB  
Article
Changes in Extremes Rainfall Events in Present and Future Climate Scenarios over the Teesta River Basin, India
by Pawan Kumar Chaubey, Rajesh Kumar Mall and Prashant K. Srivastava
Sustainability 2023, 15(5), 4668; https://doi.org/10.3390/su15054668 - 6 Mar 2023
Cited by 7 | Viewed by 2557
Abstract
Globally, changes in hydroclimate extremes such as extreme precipitation events influence water resources, natural environments, and human health and safety. During recent decades, India has observed an enormous increase in rainfall extremes during the summer monsoon (June to September) seasons. However, future extreme [...] Read more.
Globally, changes in hydroclimate extremes such as extreme precipitation events influence water resources, natural environments, and human health and safety. During recent decades, India has observed an enormous increase in rainfall extremes during the summer monsoon (June to September) seasons. However, future extreme rainfall events have significant uncertainty at the regional scale. Consequently, a comprehensive study is needed to evaluate the extreme rainfall events at a regional river basin level in order to understand the geomorphological characteristics and pattern of rainfall events. In the above purview, the current research focuses on changes in extreme rainfall events obtained through observed gridded datasets and future scenarios of climate models derived through the Coupled Model Intercomparison Project (CMIP). The results highlight a significant rise in the extremes of precipitation events during the first half of the 21st century. In addition, our study concludes that accumulated precipitation will increase by five days in the future, while the precipitation maxima will increase from 200 to 300 mm/day at the 2-year, 50-year, and 100-year return periods. Finally, it is found that during the middle of the 21st century the 23.37% number of events will increase over the TRB at the 90th percentile. Full article
(This article belongs to the Special Issue Predictive Modeling through Earth Observational Data Analysis for Natural Hazards Risk Assessment and Disaster Management)
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