Special Issue "Multi-Hazard Spatial Modelling and Mapping"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: 31 July 2021.

Special Issue Editors

Dr. Hamid Reza Pourghasemi
E-Mail Website
Guest Editor
Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran
Interests: landslide modeling; natural and man-made hazards; spatial modeling in GIS and R; machine learning techniques; multi-hazard modeling, remote sensing applications
Special Issues and Collections in MDPI journals
Prof. Dr. John P. Tiefenbacher
E-Mail Website
Guest Editor
Department of Geography, Texas State University-San Marcos, 601 University Dr. San Marcos, TX 78666, USA
Interests: environmental geography; hazards and disasters; air quality; environmental problems of the US–Mexico borderlands; human dimensions of wildlife; states of the former Soviet Union; geography of viniculture; historical environmental geography; genealogy and GIS
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Natural hazards are extreme processes or conditions in nature that yield negative impacts on human lives and properties. Reports by CRED have indicated that nearly 22.3 million people were killed by extreme events between 1900 and 2006—an average of 208,000 people annually. The evaluation of the susceptibility to hazards, their probabilities of occurrence, and likelihood of exposures of people and property is vital. The analysis of individual or multiple hazards may guide integrated management. Multi-hazard management focuses on reducing risk and mitigating exposure to events like landslides, floods, forest fires, land subsidence, avalanches, drought, earthquakes, cyclonic storms, and severe erosion. This term stresses the analysis of a combination of hazards, but in reality, all decisions are made in such a context in areas that are susceptible to these events. There are many tools (GIS, remote sensing, machine learning, meta-heuristics, multi-criteria decision-making, etc.) that can be used to support hazard management. Combinations of these can increase understanding of complex problems and may also help spawn new theories to study, explain, and manage hazards and reduce disasters. This Issue focuses on multi-hazard assessments using new methods and technologies that employ GIS, remote sensing, modeling, and artificial intelligence tools and techniques.

Dr. Hamid Reza Pourghasemi
Prof. Dr. John P. Tiefenbacher 
Guest Editors

Manuscript Submission Information

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Keywords

  • multi-hazard
  • natural disaster
  • hazard and risk mapping
  • climate change
  • drought
  • machine learning
  • meta-heuristic
  • decision support systems
  • geospatial modeling
  • spatial analysis
  • UAV
  • MCDM

Published Papers (6 papers)

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Research

Article
Wildland Fire Susceptibility Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Whale Optimization Algorithm and Simulated Annealing
ISPRS Int. J. Geo-Inf. 2021, 10(6), 382; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060382 - 04 Jun 2021
Viewed by 357
Abstract
Fires are one of the most destructive forces in natural ecosystems. This study aims to develop and compare four hybrid models using two well-known machine learning models, support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS), as well as two meta-heuristic [...] Read more.
Fires are one of the most destructive forces in natural ecosystems. This study aims to develop and compare four hybrid models using two well-known machine learning models, support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS), as well as two meta-heuristic models, the whale optimization algorithm (WOA) and simulated annealing (SA) to map wildland fires in Jerash Province, Jordan. For modeling, 109 fire locations were used along with 14 relevant factors, including elevation, slope, aspect, land use, normalized difference vegetation index (NDVI), rainfall, temperature, wind speed, solar radiation, soil texture, topographic wetness index (TWI), distance to drainage, and population density, as the variables affecting the fire occurrence. The area under the receiver operating characteristic (AUROC) was used to evaluate the accuracy of the models. The findings indicated that SVR-based hybrid models yielded a higher AUROC value (0.965 and 0.949) than the ANFIS-based hybrid models (0.904 and 0.894, respectively). Wildland fire susceptibility maps can play a major role in shaping firefighting tactics. Full article
(This article belongs to the Special Issue Multi-Hazard Spatial Modelling and Mapping)
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Article
Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models
ISPRS Int. J. Geo-Inf. 2021, 10(5), 315; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050315 - 07 May 2021
Viewed by 452
Abstract
The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life [...] Read more.
The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE), Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale. Full article
(This article belongs to the Special Issue Multi-Hazard Spatial Modelling and Mapping)
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Article
The Road Map to Classify the Potential Risk of Wind Erosion
ISPRS Int. J. Geo-Inf. 2021, 10(4), 269; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040269 - 20 Apr 2021
Viewed by 528
Abstract
Environmental degradation, for example, by wind erosion, is a serious global problem. Despite the enormous research on this topic, complex methods considering all relevant factors remain unpublished. The main intent of our paper is to develop a methodological road map to identify key [...] Read more.
Environmental degradation, for example, by wind erosion, is a serious global problem. Despite the enormous research on this topic, complex methods considering all relevant factors remain unpublished. The main intent of our paper is to develop a methodological road map to identify key soil–climatic conditions that make soil vulnerable to wind and demonstrate the road map in a case study using a relevant data source. Potential wind erosion (PWE) results from soil erosivity and climate erosivity. Soil erosivity directly reflects the wind-erodible fraction and indirectly reflects the soil-crust factor, vegetation-cover factor and surface-roughness factor. The climatic erosivity directly reflects the drought in the surface layer, erosive wind occurrence and clay soil-specific winter regime, making these soils vulnerable to wind erosion. The novelty of our method lies in the following: (1) all relevant soil–climatic data of wind erosion are combined; (2) different soil types “sand” and “clay” are evaluated simultaneously with respect to the different mechanisms of wind erosion; and (3) a methodological road map enables its application for various conditions. Based on our method, it is possible to set threshold values that, when exceeded, trigger landscape adjustments, more detailed in situ measurements or indicate the need for specific management. Full article
(This article belongs to the Special Issue Multi-Hazard Spatial Modelling and Mapping)
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Article
Assessment of Rainfall-Induced Landslide Distribution Based on Land Disturbance in Southern Taiwan
ISPRS Int. J. Geo-Inf. 2021, 10(4), 209; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040209 - 01 Apr 2021
Viewed by 356
Abstract
This study explores the impact of rainfall on the followed-up landslides after a severe typhoon and the relationship between various rainfall events and the occurrence, scale, and regional characteristics of the landslides, including second landslides. Moreover, the influence of land disturbance was evaluated. [...] Read more.
This study explores the impact of rainfall on the followed-up landslides after a severe typhoon and the relationship between various rainfall events and the occurrence, scale, and regional characteristics of the landslides, including second landslides. Moreover, the influence of land disturbance was evaluated. The genetic adaptive neural network was used in combination with the texture analysis of the geographic information system for satellite image classification and interpretation to analyze land-use change and retrieve disaster records and surface information after five rainfall events from Typhoon Morakot (2009) to Typhoon Nanmadol (2011). The results revealed that except for extreme Morakot rains, the greater the degree of slope disturbance after rain, the larger the exposed slope. Extreme rainfall similar to Morakot strikes may have a greater impact on the bare land area than on slope disturbance. Moreover, the relationship between the bare land area and the index of land disturbance condition (ILDC) is positive, and the ratio of the bare land area to the quantity of bare land after each rainfall increases with the ILDC. With higher effective accumulative rainfall on the slope in the study area or greater slope disturbance, the landslide area at the second landslide point tended to increase. Full article
(This article belongs to the Special Issue Multi-Hazard Spatial Modelling and Mapping)
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Article
Landslide Susceptibility Assessment Using an Optimized Group Method of Data Handling Model
ISPRS Int. J. Geo-Inf. 2020, 9(10), 566; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100566 - 29 Sep 2020
Viewed by 645
Abstract
Landslides can cause considerable loss of life and damage to property, and are among the most frequent natural hazards worldwide. One of the most fundamental and simple approaches to reduce damage is to prepare a landslide hazard map. Accurate prediction of areas highly [...] Read more.
Landslides can cause considerable loss of life and damage to property, and are among the most frequent natural hazards worldwide. One of the most fundamental and simple approaches to reduce damage is to prepare a landslide hazard map. Accurate prediction of areas highly prone to future landslides is important for decision-making. In the present study, for the first time, the group method of data handling (GMDH) was used to generate landslide susceptibility map for a specific region in Uzbekistan. First, 210 landslide locations were identified by field survey and then divided randomly into model training and model validation datasets (70% and 30%, respectively). Data on nine conditioning factors, i.e., altitude, slope, aspect, topographic wetness index (TWI), length of slope (LS), valley depth, distance from roads, distance from rivers, and geology, were collected. Finally, the maps were validated using the testing dataset and receiver operating characteristic (ROC) curve analysis. The findings showed that the “optimized” GMDH model (i.e., using the gray wolf optimizer [GWO]) performed better than the standalone GMDH model, during both the training and testing phase. The accuracy of the GMDH–GWO model in the training and testing phases was 94% and 90%, compared to 85% and 82%, respectively, for the standard GMDH model. According to the GMDH–GWO model, the study area included very low, low, moderate, high, and very high landslide susceptibility areas, with proportions of 14.89%, 10.57%, 15.00%, 35.12%, and 24.43%, respectively. Full article
(This article belongs to the Special Issue Multi-Hazard Spatial Modelling and Mapping)
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Article
An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping
ISPRS Int. J. Geo-Inf. 2020, 9(10), 561; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100561 - 27 Sep 2020
Cited by 2 | Viewed by 903
Abstract
In this study, we used Sentinel-1 and Sentinel-2 data to delineate post-earthquake landslides within an object-based image analysis (OBIA). We used our resulting landslide inventory map for training the data-driven model of the frequency ratio (FR) for landslide susceptibility modelling and mapping considering [...] Read more.
In this study, we used Sentinel-1 and Sentinel-2 data to delineate post-earthquake landslides within an object-based image analysis (OBIA). We used our resulting landslide inventory map for training the data-driven model of the frequency ratio (FR) for landslide susceptibility modelling and mapping considering eleven conditioning factors of soil type, slope angle, distance to roads, distance to rivers, rainfall, normalised difference vegetation index (NDVI), aspect, altitude, distance to faults, land cover, and lithology. A fuzzy analytic hierarchy process (FAHP) also was used for the susceptibility mapping using expert knowledge. Then, we integrated the data-driven model of the FR with the knowledge-based model of the FAHP to reduce the associated uncertainty in each approach. We validated our resulting landslide inventory map based on 30% of the global positioning system (GPS) points of an extensive field survey in the study area. The remaining 70% of the GPS points were used to validate the performance of the applied models and the resulting landslide susceptibility maps using the receiver operating characteristic (ROC) curves. Our resulting landslide inventory map got a precision of 94% and the AUCs (area under the curve) of the susceptibility maps showed 83%, 89%, and 96% for the F-AHP, FR, and the integrated model, respectively. The introduced methodology in this study can be used in the application of remote sensing data for landslide inventory and susceptibility mapping in other areas where earthquakes are considered as the main landslide-triggered factor. Full article
(This article belongs to the Special Issue Multi-Hazard Spatial Modelling and Mapping)
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