Landslide susceptibility prediction using artificial neural networks, SVMs and random forest: hyperparameters tuning by genetic optimization algorithm

Daviran, Mehrdad; Shamekhi, Mehdi; Ghezelbash, Reza; Maghsoudi, Abbas

doi:10.1007/s13762-022-04491-3

Cited by 46 publications

(18 citation statements)

References 49 publications

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“…After creating a landslide prediction map using several models, one of the essential steps is verifying the results' accuracy [ 126 , 127 , 128 ]. LSMs generated by five algorithms (IOE, SI, MIV, FR, and EBF) in the present investigation were validated by comparing the susceptibility map with both the training and validating datasets [ 58 , 129 ].…”

Section: Database and Methodologymentioning

confidence: 99%

GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India

Das¹,

Saha

Mitra

et al. 2023

Heliyon

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Section: Database and Methodologymentioning

confidence: 99%

GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India

Das¹,

Saha

Mitra

et al. 2023

Heliyon

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“…Hyperparameters are parameters that are set prior to the execution of the algorithm. In order to obtain more accurate models it is necessary to adjust these parameters, which can be done by the trial and error method [44]. Table 4 shows the configuration of the main hyperparameters considered for the RF model, and Table 5 shows those corresponding to the XGBoost model.…”

Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

“…Badola et al [43] conducted a landslide susceptibility analysis using XGBoost in the Chamoli district (India) and obtained a satisfactory performance. Daviran et al [44] compared three ML techniques (SVM, ANN, and RF) in the Tarom-Khalkhal sub-basin (Iran) and obtained superior performance with RF. Sahin [16] developed a framework for LSM in the Babadag district (Turkey) and obtained excellent performance with both XGBoost and RF.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods

Bravo-López

Sellers

Fernández

2023

Land

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Landslides are events that cause great impact in different parts of the world. Their destructive capacity generates loss of life and considerable economic damage. In this research, several Machine Learning (ML) methods were explored to select the most important conditioning factors, in order to evaluate the susceptibility to rotational landslides in a sector surrounding the city of Cuenca (Ecuador) and with them to elaborate landslide susceptibility maps (LSM) by means of ML. The methods implemented to analyze the importance of the conditioning factors checked for multicollinearity (correlation analysis and VIF), and, with an ML-based approach called feature selection, the most important factors were determined based on Classification and Regression Trees (CART), Feature Selection with Random Forests (FS RF), and Boruta and Recursive Feature Elimination (RFE) algorithms. LSMs were implemented with Random Forests (RF) and eXtreme Gradient Boosting (XGBoost) methods considering a landslide inventory updated to 2019 and 15 available conditioning factors (topographic (10), land cover (3), hydrological (1), and geological (1)), from which, based on the results of the aforementioned analyses, the six most important were chosen. The LSM were elaborated considering all available factors and the six most important ones, with the previously mentioned ML methods, and were compared with the result generated by an Artificial Neural Network with resilient backpropagation (ANN rprop-) with six conditioning factors. The results obtained were validated by means of AUC-ROC value and showed a good predictive capacity for all cases, highlighting those obtained with XGBoost, which, in addition to a high AUC value (>0.84), obtained a good degree of coincidence of landslides at high and very high susceptibility levels (>72%). Despite the findings of this research, it is necessary to study in depth the methods applied for the development of future research that will contribute to developing a preventive approach in the study area.

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“…There have been plenty of excellent works in landslide susceptibility evaluation (LSE, also called as landslide susceptibility mapping, LSM), and a variety of algorithms were suggested or employed in these works. These diverse methods typically consist of logic regression (Shou and Chen, 2021;Ge et al, 2022), weights of evidence (Goyes-Penafiel and Hernandez-Rojas, 2021), fuzzy logic (Nwazelibe et al, 2023), Analytical Hierarchy Process (AHP) (Wadadar and Mukhopadhyay, 2022), Information value (Es-Smairi et al, 2022), statistical index model (Berhane and Tadesse, 2021), support vector machine (SVM) (Daviran et al, 2022), random forest (RF) (Taalab et al, 2018), convolutional neural network (CNN) (Aslam et al, 2022), recurrent neural network (Ngo et al, 2021), and ensemble learning [such as boosted regression tree-random forest (Chowdhuri et al, 2021), random forest-cusp catastrophe model (Sun et al, 2022), CNN with metaheuristic optimization (Hakim et al, 2022), and so on]. Hakim et al (2022) suggested two ensemble deep learning models including the ensemble of CNN and grey wolf optimizer (GWO) and the complex model of CNN and imperialist competitive algorithm (ICA).…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility evaluation based on active deformation and graph convolutional network algorithm

Wang

et al. 2023

Front. Earth Sci.

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Disastrous landslides have become a focus of the world’s attention. Landslide susceptibility evaluation (LSE) can predict where landslides may occur and has caught the attention of scientists all over the world. This work establishes integrated criteria of potential landslide recognition and combines the historical landslides and newly-identified potential landslides to improve the accuracy, rationality, and practicability of a LSE map. Moreover, slope units can well reflect the topographic constraint to landslide occurrence and development, and Graph Convolutional Network (GCN) can well portray the topological and feature relation among various slope units. The combination of slope units and GCN is for the first time employed in LSE. This work focuses on Wanzhou District, a famous landslide-serious region in the Three Gorges reservoir area, and employs multisource data to conduct potential landslide recognition and LSE and to reveal the distribution characteristics of high landslide susceptibility. Some new viewpoints are suggested as follows. 1) The established criteria of potential landslide recognition consist of the characteristics of active deformation, stratum and lithology, tectonics, topography, micro-geomorphology, environment, meteorology, earthquakes, and human engineering activity. These criteria can well eliminate 4 types of false alarm regions and is successfully validated by field survey. 2) 34 potential landslides are newly discovered, and the movement of these potential landslides were controlled or induced by the combined action of soft-hard interbedding rock mass, steep topography, frequent tectonic movement, strong fluvial erosion, abundant precipitation, and intensive road and building construction. 3) The GCN algorithm reaches a relatively high accuracy (AUC: 0.941) and outperforms the other representative machine learning algorithms of Convolutional Neural Network (AUC: 0.926), Support Vector Machine (AUC: 0.835), and CART Tree (AUC: 0.762). 4) High landslide susceptibility is caused by the coupled action of weathered rock cavities, soft rock and swelling soil, strong river erosion, abundant rainfall, and intensive human engineering activity.

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Landslide susceptibility prediction using artificial neural networks, SVMs and random forest: hyperparameters tuning by genetic optimization algorithm

Cited by 46 publications

References 49 publications

GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India

GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India

Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods

Landslide susceptibility evaluation based on active deformation and graph convolutional network algorithm

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