Multi-hazard assessment modeling via multi-criteria analysis and GIS: a case study

Skilodimou, Hariklia D.; Bathrellos, George D.; Chousianitis, Konstantinos; Youssef, Ahmed M.; Pradhan, Biswajeet

doi:10.1007/s12665-018-8003-4

Cited by 183 publications

(70 citation statements)

References 71 publications

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“…They showed that 17.14% of the area is affected by no hazards, whereas most parts were susceptible to landslide and flood hazards together (33.70%). They also indicated accuracies of 53 applied the analytical hierarchy process (AHP) to produce separate maps for landslide, flood, and earthquake hazards and combined them into a single multi-hazard map. They showed that 80% of the landslide occurrences and all the recorded flood events fall within the boundaries of the moderate, low and very low susceptibility classes.…”

Section: Discussionmentioning

confidence: 99%

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

Pourghasemi

Kariminejad

Amiri

et al. 2020

Sci Rep

137

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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.The Sendai Framework, with its comprehensive vision, recommends more efforts to decrease disaster risk and increase sustainable development. Especially communities who are increasingly susceptible to natural hazards should adhere to these guidelines and plan accordingly. In this regard, the multi-hazard approach is often used in risk reduction projects and studies addressing risks associated with human activities or climate change on a regional and local scale 1 . It is obvious that introducing a universal set of multi-hazard assessment techniques is of fundamental importance for reducing disaster risk, and constitutes a valuable asset to share with other stakeholders, including the private sectors, local governments, and other stakeholders.The use of the term multi-hazard in the current research is related to the objective of risk reduction among natural hazards, including flood, landslides, and forest fires, in a specified spatial distribution in this study 2,3 . Recently, susceptibility modeling approaches related to single processes have advanced considerably for river floods 4 and landslides 5-7 . However, there is still neither a common terminology nor a uniform conceptual approach for analyzing multiple hazards in conjunction. This is not unexpected because multi-hazard analyses are not the sum of ...

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Section: Discussionmentioning

confidence: 99%

“…Different robustness measures for validation of the built model of each hazard.84% and 80% for flood and landslide maps, respectively Skilodimou et al (2019). …”

mentioning

confidence: 99%

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

Pourghasemi

Kariminejad

Amiri

et al. 2020

Sci Rep

137

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“…The application of this model requires grouping, standardizing, scoring, and weighting of all input parameters under the format of maps to determine the contribution of each factor in producing landslide hazards and disasters in the study area (Abella & Van Westen, 2007;Boerboom et al, 2009;Skilodimou, Bathrellos, Chousianitis, Youssef, & Pradhan, 2019). These factors do not contribute equally to causing a landslide event.…”

Section: Smce Modelmentioning

confidence: 99%

“…The SMCE is embedded within the theoretical framework initiated by (Saaty, 1990(Saaty, , 2008(Saaty, , 2014Pourghasemi et al, 2012) for spatial analysis. Furthermore, the modeling of risk and susceptibility using the SMCE method is made possible by standardizing input conditioning factors to a range of 0 to 1, and specific strategies and computation may be applicable to handle this transformation (Pourghasemi et al, 2012;Skilodimou et al, 2019). This transformation facilitates the compatibility of the model with input data sets and allows the user to produce the susceptibility as accurately as possible.…”

Section: Smce Modelmentioning

confidence: 99%

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Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda

et al. 2019

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The use of appropriate approaches to produce risk maps is critical in landslide disaster management. The aim of this study was to investigate and compare the stability index mapping (SINMAP) and the spatial multicriteria evaluation (SMCE) models for landslide risk modeling in Rwanda. The SINMAP used the digital elevation model in conjunction with physical soil parameters to determine the factor of safety. The SMCE method used six layers of landslide conditioning factors. In total, 155 past landslide locations were used for training and model validation. The results showed that the SMCE performed better than the SINMAP model. Thus, the receiver operating characteristic and three statistical estimators—accuracy, precision, and the root mean square error (RMSE)—were used to validate and compare the predictive capabilities of the two models. Therefore, the area under the curve (AUC) values were 0.883 and 0.798, respectively, for the SMCE and SINMAP. In addition, the SMCE model produced the highest accuracy and precision values of 0.770 and 0.734, respectively. For the RMSE values, the SMCE produced better prediction than SINMAP (0.332 and 0.398, respectively). The overall comparison of results confirmed that both SINMAP and SMCE models are promising approaches for landslide risk prediction in central‐east Africa.

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A multimetric approach to anthropogenic pressure analyses: A tool for measuring and mapping the human impact on the landscape

Cegielska,

Salata

2023

Land Degrad Dev

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Widespread suburbanisation and significant anthropogenic pressure contribute to multidimensional transformations in the space around us. The present research is a response to the current research gap for exploration of new multimetric methods for measuring anthropogenic pressure, mathematical and statistical analysing it, and presenting research results. The developed method could facilitate the employment of measurable parameters and expert knowledge to represent the scale and diversity of this phenomenon. To this end, the authors used universal and versatile computational GIS algorithms based on advanced geoprocessing tools to propose a set of 18 indicators as potential diagnostic variables for an econometric anthropogenic pressure model. The study area was a 10‐km ring around the Kraków city limits (Małopolskie Voivodeship, Poland), a zone under significant development and settlement pressure. Four econometric models were created using backward stepwise regression and linear regression. The diagnostic variables for the linear regression equations were selected with Hellwig's method and expert knowledge. The proposed concept was found to facilitate an effective measurement of anthropogenic pressure and an understanding of the significance of factors that affect it. It can also be useful in spatial planning decision‐making. Thanks to its versatility, the concept can be implemented for any study area for which a set of preprocessed input data is available.

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Multi-hazard assessment modeling via multi-criteria analysis and GIS: a case study

Cited by 183 publications

References 71 publications

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

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

Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda

A multimetric approach to anthropogenic pressure analyses: A tool for measuring and mapping the human impact on the landscape

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