Active-Learning Approaches for Landslide Mapping Using Support Vector Machines

Abstract

Ex post landslide mapping for emergency response and ex ante landslide susceptibility modelling for hazard mitigation are two important application scenarios that require the development of accurate, yet cost-effective spatial landslide models. However, the manual labelling of instances for training machine learning models is time-consuming given the data requirements of flexible data-driven algorithms and the small percentage of area covered by landslides. Active learning aims to reduce labelling costs by selecting more informative instances. In this study, two common active-learning strategies, uncertainty sampling and query by committee, are combined with the support vector machine (SVM), a state-of-the-art machine-learning technique, in a landslide mapping case study in order to assess their possible benefits compared to simple random sampling of training locations. By selecting more “informative” instances, the SVMs with active learning based on uncertainty sampling outperformed both random sampling and query-by-committee strategies when considering mean AUROC (area under the receiver operating characteristic curve) as performance measure. Uncertainty sampling also produced more stable performances with a smaller AUROC standard deviation across repetitions. In conclusion, under limited data conditions, uncertainty sampling reduces the amount of expert time needed by selecting more informative instances for SVM training. We therefore recommend incorporating active learning with uncertainty sampling into interactive landslide modelling workflows, especially in emergency response settings, but also in landslide susceptibility modelling.

Landslide Susceptibility Mapping of Landslides with Artificial Neural Networks: Multi-Approach Analysis of Backpropagation Algorithm Applying the Neuralnet Package in Cuenca, Ecuador

“…ML methods are implemented using different algorithms applicable to LSMs, some of them widely used, such as random forest (RF) [28] or support vector machines (SVMs), [29] and others little explored, such as eXtreme Gradient Boosting (XGBoost) [18]. A frequently applied method is artificial neural networks (ANNs), with several studies performed in different countries [7,[30][31][32][33][34][35].…”

“…According to information search in the DesInventar and EM-DAT (Emergency Events Database) databases, it was verified that during the period of 1970-2019, landslides in Ecuador caused 1642 deaths, 2456 houses destroyed, and there were losses exceeding USD 900 million [53]; 12 events were recorded during that period [54]. However, few studies were found that apply methodologies to determine landslide susceptibility in the country: Vorpahl et al [55] analyzed the contribution of statistical models to determine conditioning factors; Wang and Brenning [29] discussed an active-learning approach using SVM; and Soto et al [56] developed an approach to characterize landslides in an intramontane basin. All these studies were conducted in Loja province (southern Ecuador).…”

Evaluation of different machine learning models and novel deep learning-based algorithm for landslide susceptibility mapping

“…The basic principle of SVM is to search the optimal separating hyperplane that can maximize the interval between positive and negative samples in training dataset (Wang and Brenning 2021). Initially, SVM model was used as the supervised learning algorithm to solve binary classification problem, while the non-linear classification problem can be solved after introducing the kernel function.…”

Landslide Susceptibility Assessment Based on Different MaChine Learning Methods in Zhaoping County of Eastern Guangxi

“…The SVM ensures that the extreme solution is the global optimal solution [15]. SVM has been proven to have many unique advantages in dealing with small samples, non-linear and high-dimensional pattern recognition, and is successfully applied in disaster prediction and assessment [15,18,19,27,[30][31][32].…”

“…As a result, more and more ML approaches have been optimized and applied for landslide susceptibility assessment in different regions. Examples are: Bayesian network (BN) [26,27], Naïve Bayes (NB) [19,27], artificial neural networks (ANN) [11,20,21,28,29], SVM [13,15,[18][19][20][21][22][23][24]27,[30][31][32], Logistic Regression (LR) [11,12,15,20,23,27,33,34], decision tree (DT) [19,22,30,[35][36][37], RF [22,31,[33][34][35][38][39][40][41], SVM-LR [23], convolutional neural network (CNN)-SVM, CNN-RF and CNN-LR [42]. These have all been used to quantitatively predict and assess the susceptibility for landslide in different regions of the world.…”