Testing the performances of different fuzzy overlay methods in GIS-based landslide susceptibility mapping of Udi Province, SE Nigeria

Nwazelibe, Vincent E.; Unigwe, Chinanu O.; Egbueri, Johnbosco C.

doi:10.1016/j.catena.2022.106654

Cited by 33 publications

(25 citation statements)

References 72 publications

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“…It is projected that areas in the research region with the most rainfall will be most susceptible. When it comes to the slope, steeply sloping parts are more prone to landslides than gently sloping areas (Nwazelibe et al 2023). The study's classes with the highest likelihood of landslides occurring are those with slopes greater than 30°.…”

Section: Multicollinearity Testing On the Conditioning Factorsmentioning

confidence: 99%

“…Natural catastrophes have occurred more frequently and had more severe effects recently as a result of declining environmental quality, climate change, fast population growth, and intensive and unsuitable land-use planning (Saha et al 2021). Landslides, soil erosion, oods, earthquakes, and tsunamis, among others, have an in uence on human populations, resulting in serious injuries and fatalities, damage to residences and infrastructure, nancial loss, and social unrest (Nwazelibe et al 2023;Saha et al 2021;Lee 2018). Soil erosion frequently occurs in conjunction with landslides, and vice versa.…”

Section: Introductionmentioning

confidence: 99%

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Integrated assessment of landslide susceptibility in the Kalaleh Basin, Golestan Province, Iran using novel SVR-GOA ensemble validated with BRT, ANN, and elastic net models

Zhu

Arabameri

Santosh

et al. 2023

Preprint

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Landslides cause severe environmental problems, such as severe damages to infrastructures (i.e., bridges, roads, network masts, and buildings) and agricultural lands, across many parts of the world. Unfortunately, a high degree of accuracy in landslide mapping and prediction is still challenging due to the complicated interactions of many factors. The goal of this study was to create and introduce a new ensemble model (support vector regression–grasshopper optimization algorithm (SVR–GOA)) validated alongside artificial neural network (ANN), boosted regression tree (BRT), and elastic net models for landslide susceptibility modeling taking the Kalaleh Basin in Iran as a case study. For this objective, a total of 140 landslides were considered and 16 conditioning factors used to construct a geographic database. Subsequently, variance inflation factor and tolerance indices were used to test the multicollinearity of the hazard conditioning variables. The relative significance of these factors and their connections to the locations of the landslides were determined using random forest. The development and validation of the four landslide models, SVR-GOA, ANN, BRT, and elastic net, were then performed. This study is the first to implement the SVR-GOA in landslide mapping. The effectiveness of the models was evaluated using area under the curve (AUC), kappa, and root mean squared error values. The outcome indicated that the lithology, slope degree, rainfall, topography position index, topography wetness index, surface area, and landuse/landcover were the most influential conditioning factors. All of the models provided predictions with good degree of fit, with the SVR-GOA performing better than others. The models performed in the order, at validation phase: SVR-GOA (AUC = 0.930) > ANN (AUC = 0.833) > BRT (AUC = 0.822) > elastic net (AUC = 0.726). Our novel approach employing the SVR-GOA ensemble in landslide mapping would help advance landslide research on regional, national, and global scales.

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Section: Multicollinearity Testing On the Conditioning Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrated assessment of landslide susceptibility in the Kalaleh Basin, Golestan Province, Iran using novel SVR-GOA ensemble validated with BRT, ANN, and elastic net models

Zhu

Arabameri

Santosh

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…(5) Stream power index (SPI) is another landslide susceptibility factor that should be studied. It is a measure of the erosive power of fowing water at a given point of the topographic surface [30]. When the catchment area and slope increases, the amount of water fowing through the slope and the fow rate of water will also increase, and the fowing water will erode the surrounding slopes in the fow direction.…”

Section: Identifcation and Classifcation Of Conditional Factorsmentioning

confidence: 99%

Impact of Parameter Tuning with Genetic Algorithm, Particle Swarm Optimization, and Bat Algorithm on Accuracy of the SVM Model in Landslide Susceptibility Evaluation

Yang

Fayou

et al. 2023

Mathematical Problems in Engineering

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As one of the most serious geological hazards, landslides affect infrastructure construction. Thus, it is vital to prepare reliable landslide susceptibility evaluation maps to avoid landslide-prone areas in construction projects. More studies on landslide susceptibility using machine learning have emerged in recent years, but there is a need to study ways to draw up high-precision evaluation maps. In this article, a 60 km oil pipeline in China’s Kunming was selected as the research area. The data of 141 landslide points in the research area were obtained through field work and data collection. Meanwhile, lithology, elevation, aspect, slope, stream power index, topographic wetness index, average annual rainfall from 2017 to 2021, distance to roads, and terrain roughness were selected as causal factors of landslide susceptibility in the research area. First, the information value method was used to quantify the impact of conditional factors on landslides. Genetic algorithm (GA), particle swarm optimization (PSO), and bat algorithm (BA) were then used for parameter tuning, and the support vector machine (SVM) was used to analyze landslide susceptibility in the research area. Finally, the receiver operating characteristic (ROC) curve was used to test the model performance after parameter tuning with GA, PSO, and BA. The results show that the area-under-the-curve (AUC) values obtained through SVM, GA-SVM, PSO-SVM, and BA-SVM are 81.1%, 86.2%, 89%, and 91.8%, respectively. SVM had the best performance after parameter tuning with the BA algorithm.

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“…It is a proxy for surface saturation and soil moisture estimation in hilly terrains. TWI utilizes topographic elements of the landscape to determine whether water will flow or accumulate (Nwazelibe et al, 2023). TWI is calculated using the formula,

TWI = \ln ({normalA}_{normals} / \tan β)

…”

Section: Datasets Preparationmentioning

confidence: 99%

Application of bivariate statistical techniques for landslide susceptibility mapping: A case study in Kaghan Valley, NW Pakistan

et al. 2023

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Landslides are dangerous events that threaten both human life and property. This research presents a case study of Kaghan valley catchment, which is an area of frequent landslide activity. We aim to present a comparison between the bivariate Landslide Numerical Risk Factor (LNRF), Statistical Index (SI) and Information Value (InfV) statistical models to evaluate landslide susceptibility. A total of 1556 landslides were identified using earlier reports, field surveys, and GOOGLE Earth imagery. The abundance of landslides is primarily controlled by acute deformation caused by a major thrust fault system and proximity to Hazara Kashmir Syntaxis (HKS). A landslide inventory was randomly partitioned into two datasets. 70% (1106) of landslides were used as a training phase of the models, whereas 30% (450) as validation of the three models. A spatial database of 11 conditioning factors was produced consisting of slope, aspect, elevation, lithology, land use, Topographic Wetness Index (TWI), rainfall, Stream Power Index (SPI), distance to faults, rivers and streams. All the landslide susceptibility assessment parameters were obtained from different sources and different landslide susceptibility maps were prepared on the GIS software. Performance of the three models was validated through the Receiver operator Characteristics (ROC) through success and prediction rate curves. Results show that the area under the ROC curve (AUC) for InfV, LNRF and SI models are 70.95, 83.99 and 67.56 for success rate curves and 70.75, 83.99 and 67.85 for prediction rate curves, respectively. LNRF having the highest AUC value proved to be superior for generating regional scale landslide susceptibility maps.

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Testing the performances of different fuzzy overlay methods in GIS-based landslide susceptibility mapping of Udi Province, SE Nigeria

Cited by 33 publications

References 72 publications

Integrated assessment of landslide susceptibility in the Kalaleh Basin, Golestan Province, Iran using novel SVR-GOA ensemble validated with BRT, ANN, and elastic net models

Integrated assessment of landslide susceptibility in the Kalaleh Basin, Golestan Province, Iran using novel SVR-GOA ensemble validated with BRT, ANN, and elastic net models

Impact of Parameter Tuning with Genetic Algorithm, Particle Swarm Optimization, and Bat Algorithm on Accuracy of the SVM Model in Landslide Susceptibility Evaluation

Application of bivariate statistical techniques for landslide susceptibility mapping: A case study in Kaghan Valley, NW Pakistan

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