Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance

Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan

Heavy rainfall in mountainous terrain can trigger numerous landslides in hill slopes. These landslides can be deadly to the community living downslope with their fast pace, turning failures into catastrophic debris flows and avalanches. Active tectonics coupled with rugged topography in a complex geoenvironment multiplies this likelihood. The available hazard maps are usually helpful in mitigating disasters. However, fool-proof predicting landslide susceptibility identification remains a challenge in landslide discipline. Recently, ensemble machine learning (ML) techniques have proved the potential to provide a more accurate and efficient solution in spatial modeling. The main purposes of the current study are to examine and evaluate the predictive capability of support vector machine hybrid ensemble ML algorithms, i.e., the bagging, boosting, and stacking for modeling the catastrophic rainfall-induced landslide occurrences in the Northern parts of Kyushu Island, at the watershed scale in Japan. In this study, a landslide inventory map containing 265 landslide polygons was first interpreted from the aerial photographs and fieldwork after the September 2017 rainfall event. The raw data were randomly separated into two parts using a 70/30 sampling strategy for training and validating the landslide models. Then, 13 predisposing factors were prepared as predictors and dependent variable. The landslide susceptibility maps (LSM) were validated by the area under the receiver operating characteristic curve (AUC). The results of validation showed that the AUC values of the four models (SVM-Stacking, SVM, SVM-Bagging, and SVM-Boosting) varied from 0.74 to 0.91. The SVM-boosting model outperformed the other models, while SVM-stacking model has found to be the lowest performance. The outcome suggests that an ensemble ML model does not necessarily mean good performance. It is always preferable to select an appropriate model, such as the one proposed the hybrid novel ensemble SVM-boosting model, which could significantly improve the accuracies of LSM. Also, from Information Gain Ratio (IGR) we found that the rainfall factor mainly affects the results, that agrees with the analogy of present study.

Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan

The main objective of the current study was to introduce a Deep Learning Neural Network (DLNN) model in landslide susceptibility assessments and compare its predictive performance with state-of-the-art machine learning models. The efficiency of the DLNN model was estimated for the Kon Tum Province, Viet Nam, an area characterized by the presence of landslide phenomena. Nine landslide related variables, elevation, slope angle, aspect, land use, normalized difference vegetation index, soil type, distance to faults, distance to geology boundaries, lithology cover, and 1,657 landslide locations, were used so as to produce the training and validation datasets during the landslide susceptibility assessment. The Frequency Ratio method was used so as to estimate the existing relation between the landslide-related variables and the presence of landslides, assigning to each variable class a weight value. Based on the results concerning the predictive ability of the landslide related variables which was evaluated using the Information ration method, all variables were further processed since they appear as highly predictive. The learning ability of the DLNN model has been evaluated and compared with a Multi Layer Preceptron Neural Network, a Support Vector Machine, a C4.5-Decision Tree model and a Random Forest model using the training dataset, whereas the predictive performance of each model has been evaluated and compared using the validation datasets. In order to evaluate their learning and predictive capacity of each model the classification accuracy, the sensitivity, the specificity and the area under the success and predictive rate curves (AUC) were calculated. Results showed that the proposed DLNN model had a higher performance than the four benchmark models. Although DLNN has been used seldom in landslide susceptibility assessments, the study highlights that the usage of deep learning approach could be considered as a satisfactory alternative approach for landslide susceptibility mapping.

Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment

Landslide risk prevention requires the delineation of landslide-prone areas as accurately as possible. Therefore, selecting a method or a technique that is capable of providing the highest landslide prediction capability is highly important. The main objective of this study is to assess and compare the prediction capability of advanced machine learning methods for landslide susceptibility mapping in the Mila Basin (Algeria). First, a geospatial database was constructed from various sources. The database contains 1156 landslide polygons and 16 conditioning factors (altitude, slope, aspect, topographic wetness index (TWI), landforms, rainfall, lithology, stratigraphy, soil type, soil texture, landuse, depth to bedrock, bulk density, distance to faults, distance to hydrographic network, and distance to road networks). Subsequently, the database was randomly resampled into training sets and validation sets using 5 times repeated 10 k-folds cross-validations. Using the training and validation sets, five landslide susceptibility models were constructed, assessed, and compared using Random Forest (RF), Gradient Boosting Machine (GBM), Logistic Regression (LR), Artificial Neural Network (NNET), and Support Vector Machine (SVM). The prediction capability of the five landslide models was assessed and compared using the receiver operating characteristic (ROC) curve, the area under the ROC curves (AUC), overall accuracy (Acc), and kappa index. Additionally, Wilcoxon signed-rank tests were performed to confirm statistical significance in the differences among the five machine learning models employed in this study. The result showed that the GBM model has the highest prediction capability (AUC = 0.8967), followed by the RF model (AUC = 0.8957), the NNET model (AUC = 0.8882), the SVM model (AUC = 0.8818), and the LR model (AUC = 0.8575). Therefore, we concluded that GBM and RF are the most suitable for this study area and should be used to produce landslide susceptibility maps. These maps as a technical framework are used to develop countermeasures and regulatory policies to minimize landslide damages in the Mila Basin. This research demonstrated the benefit of selecting the best-advanced machine learning method for landslide susceptibility assessment.

Boumezbeur Abderrahmane

Papers

Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance

Landslide Susceptibility Assessment at Mila Basin (Algeria): A Comparative Assessment of Prediction Capability of Advanced Machine Learning Methods

Unsaturated soil slope properties and shallow landslides development in Souk Ahras area, NE, Algeria