Multi-Hazard Susceptibility Assessment Using the Analytical Hierarchy Process and Frequency Ratio Techniques in the Northwest Himalayas, Pakistan

Rehman, Adnanul; Song, Jinxi; Haq, Fazlul; Mahmood, Shakeel; Ahamad, Muhammad İ̇rfan; Basharat, Muhammad; Sajid, Muhammad; Mehmood, Muhammad Sajid

doi:10.3390/rs14030554

Cited by 43 publications

(21 citation statements)

References 69 publications

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“…In the present investigation, the Seed Cell Area Index (SCAI) method was used to assess the significance of each applied model. SCAI enables proportional calibration between the test dataset with derived landslide susceptibility zones by each applied model (Süzen and Doyuran 2004;Li et al 2021a, b;Rehman et al 2022).…”

Section: Importance Of Implemented Models and Key Parametermentioning

confidence: 99%

Spatial implementation of frequency ratio, statistical index and index of entropy models for landslide susceptibility mapping in Al-Balouta river basin, Tartous Governorate, Syria

et al. 2022

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Landslide vulnerability prediction maps are among the most important tools for managing natural hazards associated with slope stability in river basins that affect ecosystems, properties, infrastructure and society. Landslide events are among the most hazardous patterns of slope instability in the coastal mountains of Syria. Thus, the main goals of this research are to evaluate the performance of three different statistical outputs: Frequency Ratio (FR), Statistical Index (SI) and Index of Entropy (IoE) and therefore map landslide susceptibility in the coastal region of Syria. To this end, we identified a total of 446 locations of landslide events, based on the preliminary inventory map derived from fieldwork and high-resolution imagery surveys. In this regard, 13 geo-environmental factors that have a high influence on landslides were selected for landslide susceptibility mapping. The results indicated that the FR method outperformed the SI and IoE models with a high AUC of 0.824 and better adaptability, followed by the SI with 0.791. According to the SCAI values, although the FR model achieved the best reliability, the other two models also showed good capability in determining landslide susceptibility. The result of FR-based modelling showed that 18.51 and 19.98% of the study area fall under the high and very high landslide susceptible categories, respectively. In the map generated by the SI method, about 36% of the study area is classified as having high or very high landslide sensitivity. In the IoE method, whereas 14.18 and 25.62% of the study area were classified as “very high susceptible” and “high susceptible,” respectively. The relative importance analysis demonstrated that the slope aspects, lithology and proximity to roads effectively motivated the acceleration of slope material instability and were the most influential in both the FR and SI models. On the other hand, the IoE model indicated that the proximity to faults and roads, along with the lithology factor, were important influences in the formation of landslide events. As a result, the statistical bivariate models-based landslide mapping provided a reliable and systematic approach to guide the long-term strategic planning procedures in the study area.

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Section: Importance Of Implemented Models and Key Parametermentioning

confidence: 99%

Spatial implementation of frequency ratio, statistical index and index of entropy models for landslide susceptibility mapping in Al-Balouta river basin, Tartous Governorate, Syria

et al. 2022

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“…Statistical methods are usually constructed using linear analysis between historical landslides and predisposing factors and are widely used in LSM due to their simple principles and efficient computations. Rehman et al [8] used Muzaffarabad as the study area and calculated the subjective and objective weights of all conditioning factors and their categories using AHP and FR. In addition, Lin et al [9] explored the application of the IVM method in geological hazard evaluation to provide a scientific basis for the development of regional geological hazard prevention and control strategies.…”

Section: Introductionmentioning

confidence: 99%

A Novel Heterogeneous Ensemble Framework Based on Machine Learning Models for Shallow Landslide Susceptibility Mapping

Tang,

Wang,

et al. 2023

Remote Sensing

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Landslides are devastating natural disasters that seriously threaten human life and property. Landslide susceptibility mapping (LSM) plays a key role in landslide hazard management. Machine learning (ML) models are widely used in LSM but suffer from limitations such as overfitting and unreliable accuracy. To improve the classification performance of a single machine learning (ML) model, this study selects logistic regression (LR), support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT), and proposes a novel heterogeneous ensemble framework based on Bayesian optimization (BO), namely, stratified weighted averaging (SWA), to test its applicability in a typical landslide area in Yanbian Prefecture, China. Firstly, a dataset consisting of 1531 historical landslides was collected from field investigations and historical records, and a spatial database containing 16 predisposing factors was established. The dataset was divided into a training set and a test set in a ratio of 7:3. The results showed that SWA effectively improved the Accuracy, AUC, and robustness of the model compared to a single ML model. The SWA achieved the best classification results (Accuracy = 91.39% and AUC = 0.967). To verify the generalization ability of SWA, we selected published landslide datasets from Yanshan country and Yongxin country in China for testing. SWA also performed well, with an AUC of 0.871 and 0.860, respectively. As indicated by shapely values (SVs), Normalized Difference Vegetation Index (NDVI) is the factor that has the greatest impact on landslide occurrence. The landslide susceptibility maps obtained from this study will provide an effective reference program for land use planning and disaster prevention and mitigation projects in Yanbian Prefecture, China.

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“…According to recent studies (Cai et al, 2018;Peng et al, 2018;Kafy et al, 2021a,b), the average LST in cities is typically 2-4°C higher than in rural areas, which is mostly contributed by the removal of green cover and installation of gray infrastructure. In addition, the increase in the impervious surface makes societies more vulnerable to flooding due to the higher flow velocity of the water (Rehman et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Evaluating land use/cover change associations with urban surface temperature via machine learning and spatial modeling: Past trends and future simulations in Dera Ghazi Khan, Pakistan

Mehmood

Rehman²,

Sajjad³

et al. 2023

Front. Ecol. Evol.

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While urbanization puts lots of pressure on green areas, the transition of green-to-grey surfaces under land use land cover change is directly related to increased land surface temperature–compromising livability and comfort in cities due to the heat island effect. In this context, we evaluate historical and future associations between land use land cover changes and land surface temperature in Dera Ghazi Khan–one of the top cities in Pakistan–using multi-temporal Landsat data over two decades (2002–2022). After assessing current land use changes and future predictions, their impact on land surface temperature and urban heat island effect is measured using machine learning via Multi-Layer Perceptron-Markov Chain, Artificial Neural Network and Cellular Automata. Significant changes in land use land cover were observed in the last two decades. The built-up area expanded greatly (874 ha) while agriculture land (−687 ha) and barren land (−253 ha) show decreasing trend. The water bodies were found the lowest changes (57 ha) and vegetation cover got the largest proportion in all the years. This green-grey conversion in the last two decades (8.7%) and prospect along the main corridors show the gravity of unplanned urban growth at the cost of vegetation and agricultural land (−6.8%). The land surface temperature and urban heat island effect shows a strong positive correlation between urbanization and vegetation removal. The simulation results presented in this study confirm that by 2032, the city will face a 5° C high mean temperature based on historical patterns, which could potentially lead to more challenges associated with urban heat island if no appropriate measures are taken. It is expected that due to land cover changes by 2032, ~60% of urban and peri-urban areas will experience very hot to hot temperatures (> 31.5°C). Our results provide baseline information to urban managers and planners to understand the increasing trends of land surface temperature in response to land cover changes. The study is important for urban resource management, sustainable development policies, and actions to mitigate the heat island effect. It will further asset the broader audience to understand the impact of land use land cover changes on the land surface temperature and urban heat island effect in the light of historic pattern and machine learning approach.

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Multi-Hazard Susceptibility Assessment Using the Analytical Hierarchy Process and Frequency Ratio Techniques in the Northwest Himalayas, Pakistan

Cited by 43 publications

References 69 publications

Spatial implementation of frequency ratio, statistical index and index of entropy models for landslide susceptibility mapping in Al-Balouta river basin, Tartous Governorate, Syria

Spatial implementation of frequency ratio, statistical index and index of entropy models for landslide susceptibility mapping in Al-Balouta river basin, Tartous Governorate, Syria

A Novel Heterogeneous Ensemble Framework Based on Machine Learning Models for Shallow Landslide Susceptibility Mapping

Evaluating land use/cover change associations with urban surface temperature via machine learning and spatial modeling: Past trends and future simulations in Dera Ghazi Khan, Pakistan

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