Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data

Kalantar, Bahareh; Ueda, Naonori; Saeidi, Vahideh; Ahmadi, Kourosh; Halin, Alfian Abdul; Shabani, Farzin

doi:10.3390/rs12111737

Cited by 96 publications

(30 citation statements)

References 60 publications

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“…Multi-collinearity analysis always gives the perfect outcome to evaluate the linear dependency of different geo-environmental factors in an ML model [ 15 , 36 ]. It is a statistical analysis and is able to find two variables of high correlation in a multiple regression study.…”

Section: Methodsmentioning

confidence: 99%

“…TP is when gully pixels are correctly classified as a gully, and FP is when gully pixels are incorrectly classified as a gully. On the other hand, if gully pixels are correctly or incorrectly classified as non-gully, then they are TN and FN, respectively [ 36 ]. If higher values are found among these statistical indices then the model gives better results and vice versa [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

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Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Band

Janizadeh

Pal

et al. 2020

Sensors

114

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This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Band

Janizadeh

Pal

et al. 2020

Sensors

114

View full text Add to dashboard Cite

show abstract

“…Multi-collinearity analysis always gives the perfect outcome to evaluate the linear dependency of different geo-environmental factors in a ML model [15,38]. Basically, it is a statistical analysis and found among the two variables of high correlation in a multiple regression study.…”

Section: Multi-collinearity Analysismentioning

confidence: 99%

“…TP, when gully pixels are correctly classified as gully and FP when gully pixels are incorrectly classified as gully. On the other hand, if gully pixels are correctly or incorrectly classified as non-gully then they are TN and FN respectively [38]. If higher values are found among these statistical indices then model gives better results and vice-versa [23].…”

Section: Statistical Indicesmentioning

confidence: 99%

Novel Ensemble Approach of Deep Learning Neural Network Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Shahab¹,

Janizadeh²,

Pal³

et al. 2020

Preprint

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This study aims to evaluate a new approach in modeling gully erosion susceptibility based on deep learning neural network (DLNN) model, ensemble Particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN) and comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shiran watershed, Iran. For this purpose, 13 independent variables affecting gully erosion susceptibility in the study area, including altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from river, land use, soil, lithology, rainfall, , stream power index (SPI), topographic wetness index (TWI), were prepared. Also, 132 gully erosion locations were identified during field visits. Data for modeling were divided into two categories of training (70%) and testing (30%). Receiver operating characteristic (ROC) parameters including sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV) and area under curve (AUC) were used to evaluate the performance of the models. The results showed that, the AUC values from ROC with considering testing datasets of PSO-DLNN is 0.89 and which is associated with superb accuracy. Rest of the models also associated with optimal accuracy and near about PSO-DLNN model; the AUC values from ROC of DLNN, SVM and ANN for testing datasets are 0.87, 0.85 and 0.84 respectively. The PSO algorithm has updated and optimized the weights of DLNN model, and as a result, the efficiency of this model in predicting gully erosion susceptibility has increased. Therefore, it can be concluded that the use of DLNN model and its ensemble with PSO algorithm can be used as a novel and practical method in predicting the susceptibility of gully erosion that helps planners and managers in managing and reducing the risk of this phenomenon.

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“…The development of remote sensing technologies in the last few decades enables researchers to map landslide susceptibility more efficiently, due to the availability of high spatial and temporal resolution data [3,4,6]. For instance, high-resolution remote sensing (satellite imagery) data are used to develop various thematic layers explaining the topography, land cover, geology, and hydrology, which are essential parameters for predicting landslides [4,7]. Remote sensing techniques are also useful in developing accurate landslide inventory maps [3,6].…”

Section: Introductionmentioning

confidence: 99%

Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping

et al. 2020

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Despite yielding considerable degrees of accuracy in landslide predictions, the outcomes of different landslide susceptibility models are prone to spatial disagreement; and therefore, uncertainties. Uncertainties in the results of various landslide susceptibility models create challenges in selecting the most suitable method to manage this complex natural phenomenon. This study aimed to propose an approach to reduce uncertainties in landslide prediction, diagnosing spatial agreement in machine learning-based landslide susceptibility maps. It first developed landslide susceptibility maps of Cox’s Bazar district of Bangladesh, applying four machine learning algorithms: K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM), featuring hyperparameter optimization of 12 landslide conditioning factors. The results of all the four models yielded very high prediction accuracy, with the area under the curve (AUC) values range between 0.93 to 0.96. The assessment of spatial agreement of landslide predictions showed that the pixel-wise correlation coefficients of landslide probability between various models range from 0.69 to 0.85, indicating the uncertainty in predicted landslides by various models, despite their considerable prediction accuracy. The uncertainty was addressed by establishing a Logistic Regression (LR) model, incorporating the binary landslide inventory data as the dependent variable and the results of the four landslide susceptibility models as independent variables. The outcomes indicated that the RF model had the highest influence in predicting the observed landslide locations, followed by the MLP, SVM, and KNN models. Finally, a combined landslide susceptibility map was developed by integrating the results of the four machine learning-based landslide predictions. The combined map resulted in better spatial agreement (correlation coefficients range between 0.88 and 0.92) and greater prediction accuracy (0.97) compared to the individual models. The modelling approach followed in this study would be useful in minimizing uncertainties of various methods and improving landslide predictions.

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Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data

Cited by 96 publications

References 60 publications

Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Novel Ensemble Approach of Deep Learning Neural Network Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping

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