scholarly journals Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco, Italy

2021 ◽  
Author(s):  
Mohammad Mehrabi
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Learning Approaches ◽  
Landslide Susceptibility Zonation

scholarly journals Comparison between Deep Learning and Tree-Based Machine Learning Approaches for Landslide Susceptibility Mapping

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2664
Author(s):  
Sunil Saha ◽  
Jagabandhu Roy ◽  
Tusar Kanti Hembram ◽  
Biswajeet Pradhan ◽  
Abhirup Dikshit ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Landslide Susceptibility ◽  
Learning Model ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Learning Approaches ◽  
Statistical Measures ◽  
Deep Learning Model

The efficiency of deep learning and tree-based machine learning approaches has gained immense popularity in various fields. One deep learning model viz. convolution neural network (CNN), artificial neural network (ANN) and four tree-based machine learning models, namely, alternative decision tree (ADTree), classification and regression tree (CART), functional tree and logistic model tree (LMT), were used for landslide susceptibility mapping in the East Sikkim Himalaya region of India, and the results were compared. Landslide areas were delimited and mapped as landslide inventory (LIM) after gathering information from historical records and periodic field investigations. In LIM, 91 landslides were plotted and classified into training (64 landslides) and testing (27 landslides) subsets randomly to train and validate the models. A total of 21 landslide conditioning factors (LCFs) were considered as model inputs, and the results of each model were categorised under five susceptibility classes. The receiver operating characteristics curve and 21 statistical measures were used to evaluate and prioritise the models. The CNN deep learning model achieved the priority rank 1 with area under the curve of 0.918 and 0.933 by using the training and testing data, quantifying 23.02% and 14.40% area as very high and highly susceptible followed by ANN, ADtree, CART, FTree and LMT models. This research might be useful in landslide studies, especially in locations with comparable geophysical and climatological characteristics, to aid in decision making for land use planning.

Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches

CATENA ◽  
2019 ◽  
Vol 175 ◽  
pp. 203-218 ◽  
Author(s):  
Binh Thai Pham ◽  
Indra Prakash ◽  
Sushant K. Singh ◽  
Ataollah Shirzadi ◽  
Himan Shahabi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Learning Approaches ◽  
Susceptibility Modeling ◽  
Ensemble Techniques ◽  
Hybrid Machine

Decision tree based ensemble machine learning approaches for landslide susceptibility mapping

2021 ◽  
pp. 1-35
Author(s):  
Alireza Arabameri ◽  
Subodh Chandra Pal ◽  
Fatemeh Rezaie ◽  
Rabin Chakrabortty ◽  
Asish Saha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Learning Approaches ◽  
Ensemble Machine Learning

scholarly journals Factors Affecting Landslide Susceptibility Mapping: Assessing the Influence of Different Machine Learning Approaches, Sampling Strategies and Data Splitting

Land ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 989
Author(s):  
Minu Treesa Abraham ◽  
Neelima Satyam ◽  
Revuri Lokesh ◽  
Biswajeet Pradhan ◽  
Abdullah Alamri
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Sampling Strategy ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Susceptibility Map ◽  
Learning Approaches ◽  
Sampling Strategies ◽  
Data Splitting

Data driven methods are widely used for the development of Landslide Susceptibility Mapping (LSM). The results of these methods are sensitive to different factors, such as the quality of input data, choice of algorithm, sampling strategies, and data splitting ratios. In this study, five different Machine Learning (ML) algorithms are used for LSM for the Wayanad district in Kerala, India, using two different sampling strategies and nine different train to test ratios in cross validation. The results show that Random Forest (RF), K Nearest Neighbors (KNN), and Support Vector Machine (SVM) algorithms provide better results than Naïve Bayes (NB) and Logistic Regression (LR) for the study area. NB and LR algorithms are less sensitive to the sampling strategy and data splitting, while the performance of the other three algorithms is considerably influenced by the sampling strategy. From the results, both the choice of algorithm and sampling strategy are critical in obtaining the best suited landslide susceptibility map for a region. The accuracies of KNN, RF, and SVM algorithms have increased by 10.51%, 10.02%, and 4.98% with the use of polygon landslide inventory data, while for NB and LR algorithms, the performance was slightly reduced with the use of polygon data. Thus, the sampling strategy and data splitting ratio are less consequential with NB and algorithms, while more data points provide better results for KNN, RF, and SVM algorithms.

scholarly journals Landslide Susceptibility Zonation Using Statistical and Machine Learning Approaches in Northern Lecco, Italy

2021 ◽  
Author(s):  
Mohammad Mehrabi
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Learning Approaches ◽  
Visual Interpretation ◽  
Topographic Wetness Index ◽  
Inference System ◽  
Conditioning Factors ◽  
Susceptibility Maps

Abstract This study deals with landslide susceptibility mapping in the northern part of Lecco Province, Lombardy Region, Italy. In so doing, a valid landslide inventory map and thirteen conditioning factors (including elevation, slope aspect, slope degree, plan curvature, profile curvature, distance to waterway, distance to road, distance to fault, soil type, land use, lithology, stream power index, and topographic wetness index) form the spatial database within geographic information system (GIS). The used evaluative models comprise a bivariate statistical approach called frequency ratio (FR) and two machine learning tools, namely multi-layer perceptron neural network (MLPNN) and adaptive neuro-fuzzy inference system (ANFIS). These models first use landslide and non-landslide records for comprehending the relationship between the landslide occurrence and conditioning factors. Then landslide susceptibility values are predicted for the whole area. The accuracy of the produced susceptibility maps is measured using area under the curve (AUC) index, according to which, the MLPNN (AUC = 0.916) presented the most accurate map, followed by the FR (AUC = 0. 898) and ANFIS (AUC = 0.889). Visual interpretation of the susceptibility maps, FR-based correlation analysis, as well as the importance assessment of conditioning factors, all indicated the significant contribution of the road networks to the crucial susceptibility of landslide. Lastly, an explicit predictive formula is extracted from the implemented MLPNN model for a convenient approximation of landslide susceptibility value.

scholarly journals An Experimental Research on the Use of Recurrent Neural Networks in Landslide Susceptibility Mapping

2019 ◽  
Vol 8 (12) ◽  
pp. 578 ◽  
Author(s):  
Begum Mutlu ◽  
Hakan A. Nefeslioglu ◽  
Ebru A. Sezer ◽  
M. Ali Akcayol ◽  
Candan Gokceoglu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Landslide Susceptibility ◽  
Learning Algorithm ◽  
Landslide Susceptibility Mapping ◽  
Learning Approaches ◽  
Characteristic Analysis ◽  
Initiation Point ◽  
Landslide Mapping ◽  
The Individual

Natural hazards have a great number of influencing factors. Machine-learning approaches have been employed to understand the individual and joint relations of these factors. However, it is a challenging process for a machine learning algorithm to learn the relations of a large parameter space. In this circumstance, the success of the model is highly dependent on the applied parameter reduction procedure. As a state-of-the-art neural network model, representative learning assumes full responsibility of learning from feature extraction to prediction. In this study, a representative learning technique, recurrent neural network (RNN), was applied to a natural hazard problem. To that end, it aimed to assess the landslide problem by two objectives: Landslide susceptibility and inventory. Regarding the first objective, an empirical study was performed to explore the most convenient parameter set. In landslide inventory studies, the capability of the implemented RNN on predicting the subsequent landslides based on the events before a certain time was investigated respecting the resulting parameter set of the first objective. To evaluate the behavior of implemented neural models, receiver operating characteristic analysis was performed. Precision, recall, f-measure, and accuracy values were additionally measured by changing the classification threshold. Here, it was proposed that recall metric be utilized for an evaluation of landslide mapping. Results showed that the implemented RNN achieves a high estimation capability for landslide susceptibility. By increasing the network complexity, the model started to predict the exact label of the corresponding landslide initiation point instead of estimating the susceptibility level.

scholarly journals Integrating InSAR Observables and Multiple Geological Factors for Landslide Susceptibility Assessment

2021 ◽  
Vol 11 (16) ◽  
pp. 7289
Author(s):  
Yan-Ting Lin ◽  
Yi-Keng Chen ◽  
Kuo-Hsin Yang ◽  
Chuin-Shan Chen ◽  
Jen-Yu Han
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Confusion Matrix ◽  
Model Performance ◽  
Learning Approaches ◽  
Displacement Fields ◽  
Sensing Technology ◽  
Integrated Technique ◽  
Improved Model ◽  
The Impact

Due to extreme weather, researchers are constantly putting their focus on prevention and mitigation for the impact of disasters in order to reduce the loss of life and property. The disaster associated with slope failures is among the most challenging ones due to the multiple driving factors and complicated mechanisms between them. In this study, a modern space remote sensing technology, InSAR, was introduced as a direct observable for the slope dynamics. The InSAR-derived displacement fields and other in situ geological and topographical factors were integrated, and their correlations with the landslide susceptibility were analyzed. Moreover, multiple machine learning approaches were applied with a goal to construct an optimal model between these complicated factors and landslide susceptibility. Two case studies were performed in the mountainous areas of Taiwan Island and the model performance was evaluated by a confusion matrix. The numerical results revealed that among different machine learning approaches, the Random Forest model outperformed others, with an average accuracy higher than 80%. More importantly, the inclusion of the InSAR data resulted in an improved model accuracy in all training approaches, which is the first to be reported in all of the scientific literature. In other words, the proposed approach provides a novel integrated technique that enables a highly reliable analysis of the landslide susceptibility so that subsequent management or reinforcement can be better planned.

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