Improving spatial agreement in machine learning-based landslide susceptibility mapping

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

Landslide Susceptibility Mapping Using the Stacking Ensemble Machine Learning Method in Lushui, Southwest China

Landslide susceptibility mapping is considered to be a prerequisite for landslide prevention and mitigation. However, delineating the spatial occurrence pattern of the landslide remains a challenge. This study investigates the potential application of the stacking ensemble learning technique for landslide susceptibility assessment. In particular, support vector machine (SVM), artificial neural network (ANN), logical regression (LR), and naive Bayes (NB) were selected as base learners for the stacking ensemble method. The resampling scheme and Pearson’s correlation analysis were jointly used to evaluate the importance level of these base learners. A total of 388 landslides and 12 conditioning factors in the Lushui area (Southwest China) were used as the dataset to develop landslide modeling. The landslides were randomly separated into two parts, with 70% used for model training and 30% used for model validation. The models’ performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and statistical measures. The results showed that the stacking-based ensemble model achieved an improved predictive accuracy as compared to the single algorithms, while the SVM-ANN-NB-LR (SANL) model, the SVM-ANN-NB (SAN) model, and the ANN-NB-LR (ANL) models performed equally well, with AUC values of 0.931, 0.940, and 0.932, respectively, for validation stage. The correlation coefficient between the LR and SVM was the highest for all resampling rounds, with a value of 0.72 on average. This connotes that LR and SVM played an almost equal role when the ensemble of SANL was applied for landslide susceptibility analysis. Therefore, it is feasible to use the SAN model or the ANL model for the study area. The finding from this study suggests that the stacking ensemble machine learning method is promising for landslide susceptibility mapping in the Lushui area and is capable of targeting areas prone to landslides

Towards the optimal Pixel size of dem for automatic mapping of landslide areas

Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1m, 2m, 5m and 10m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5m DEM-resolution for FFNN and 1m DEM resolution for results. The best performance was found to be using 5m DEM-resolution for FFNN and 1m DEM resolution for ML classification

머신러닝 기법을 활용한 기후변화 영향에 따른 재해 리스크 평가

학위논문(박사) -- 서울대학교대학원 : 환경대학원 협동과정 조경학, 2022. 8. 이동근.기후 변화는 우리 세대에게 시급한 위협이다. 자연 재해는 기후 변화로 인해 더 잦은 빈도와 강력하게 발생하고 있어 예측불가성이 커져가고 있다. 특히, 한국의 자연재해는 대부분 기상 현상으로 인해 발생하는데, 지난 10년간 재해로 인한 전체 피해는 주로 태풍(49%)과 호우(40%)에 기인하였다. 따라서 장기적으로 대비하기 위해서는 홍수, 산사태 등 호우와 관련된 위험을 분석하고 평가하는 위험관리가 필요하다. 따라서 본 논문의 주요 연구질문은 다음과 같다: 1) 기후변화로 인한 복잡한 상황에서 다양한 요인을 고려하여 미래의 잠재적 위험을 어떻게 예측할 것인가, 2) 이러한 위험을 줄이기 위해 어떤 노력을 하는 것이 지속가능한가?. 먼저 연안 홍수, 산사태 등 복합적 영향의 미래 위험도를 평가하기 위해 첫째, 최근 연구에서 널리 활용되고 있는 다중 머신러닝(ML) 알고리즘을 확률론적 접근 방식으로 활용하여 현재의 위험도를 분석하였다. 다양한 RCP 기후변화 시나리오 및 지역 기후 모델에 따른 예측 강우량을 고려하여 미래 위험을 추정했습니다. 둘째, 기후변화 영향으로 인한 재난위험 대응을 위한 적응전략의 실효성을 평가하기 위하여, 적응전략으로 중요한 역할을 하는 녹지, 방파제 등 구조적 대책의 효과성과 지속가능성을 여러 적응경로로 나눠 연안침수에 대한 위험저감을 평가하였다. 연구의 결과는 미래의 위험 지역을 식별하고 위험 관리를 위한 의사 결정 과정, 그리고 토지 이용 계획 및 의사 결정 프로세스를 포함한 재난 감소 및 관리 조치에 대해 지원 가능할 것이다.Climate change is an urgent threat to our generation. Natural hazards have become more unpredictable, occurring more frequently and with greater force, due to climate change. Natural disasters in Korea are mostly caused by meteorological events. The total damage caused by disasters in the last ten years is attributed mainly to typhoons (49%) and heavy rain (40%). Therefore, risk management, which analyzes and evaluates hazard risk related to heavy rainfall such as flooding and landslides, is needed to prepare for the long term. Also, effective monitoring and detection responses to climate change are critical for predicting and managing threats to hazard risks. Therefore, the main research questions of this thesis are as follows: 1) How to predict future potential risks in a complex situation due to climate change considering various factors, 2) And what kind of efforts are made to reduce such risks? Is it sustainable? First of all, to assess the future risk of multiple hazards such as coastal flooding, landslide, 1) this study analyzed the present risk by using multiple machine learning (ML) algorithms that have been widely used in recent studies as part of probabilistic approaches, and future risks were estimated by considering the forecasted rainfall according to different representative concentration pathway (RCP) climate change scenarios and regional climate models. Secondly, to evaluate the effectiveness of adaptation strategies to respond to disaster risks posed by climate change impacts, 2) this research analyzed the effectiveness and sustainability of structural measures such as green space and seawall, which are widely used and play an important role as countermeasures against coastal flooding, by dividing into several adaptation pathways. The results of this study identify future at-risk areas and can support decision-making for risk management and can guide disaster reduction and management measures, including land use planning and decision-making processes.Abstract i Chapter 1. Introduction ２ 1. Background ２ 2. Purpose ４ Chapter 2. Prediction of coastal flooding risk under climate change impacts in South Korea using machine learning algorithms ７ 1. Introduction ７ 2. Materials and Method ９ 2.1 Study Area ９ 2.2 Machine learning algorithms １０ 2.3 Method １１ 3. Results １５ 3.1 Comparison of ML algorithms １５ 3.2 Risk probability map １６ 3.3 Future risk under climate change impacts １７ 4. Discussion １８ 4.1 Regional differences １８ 4.2 Significance factor ２０ 4.3 Methodological implications ２１ 5. Conclusions ２２ Chapter 3. Predicting susceptibility to landslides under climate change impacts in metropolitan areas of South Korea using machine learning ２５ 1. Introduction ２５ 2. Materials and Method ２８ 2.1 Study Area ２８ 2.2 Data ２９ 2.3 Landslide factors analysis ３０ 2.4 Machine learning algorithms and validation ３２ 2.5 LSA using different algorithms ３３ 2.6 Predicting landslide susceptibility ３４ 3. Results ３５ 3.1 Multi-collinearity and influencing factor analysis ３５ 3.2 Comparison of machine learning algorithms ３７ 3.3 Predicting landslide susceptibility ３８ 4. Discussion ３９ 4.1 Analysis of results from different ML algorithms ３９ 4.2 Difference in susceptibilities based on land cover type ４０ 5. Conclusions ４１ Chapter 4. Adaptation strategies to future coastal flooding: performance evaluation of green and grey infrastructure in South Korea ４３ 1. Introduction ４３ 2. Materials and Method ４６ 2.1 Study area ４６ 2.2 Data ４７ 2.3 Comparison of machine learning (ML) techniques and coastal flooding risk analysis ４９ 2.4 Evaluation of coastal flooding risk with ASs ５０ 2.5 Potential coastal flooding risk depending on different adaptive pathways ５１ 3. Results ５３ 3.1 Performances of ML algorithms ５３ 3.2 Coastal flooding risk with ASs ５４ 3.3 Potential coastal flooding risk according to different adaptive pathways ５６ 4. Discussion ５９ 4.1 Effect of AS according to spatial characteristics ５９ 4.2 Importance of nature-based solutions as ASs ６２ 5. Conclusion ６３ Chapter 5. Conclusion ６６ Bibliography ７１ Abstract in Korean ８６박

Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan Plateau region by five learning algorithms

AbstractLandslides are considered as major natural hazards that cause enormous property damages and fatalities in Qinghai-Tibetan Plateau (QTP). In this article, we evaluated the landslide susceptibility, and its spatial differencing in the whole Qinghai-Tibetan Plateau region using five state-of-the-art learning algorithms; deep neural network (DNN), logistic regression (LR), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM), differing from previous studies only in local areas of QTP. The 671 landslide events were considered, and thirteen landslide conditioning factors (LCFs) were derived for database generation, including annual rainfall, distance to drainage $${(\mathrm{Ds}}_{\mathrm{d}})$$ ( Ds d ) , distance to faults $${(\mathrm{Ds}}_{\mathrm{f}})$$ ( Ds f ) , drainage density ($${D}_{d})$$ D d ) , elevation (Elev), fault density $$({F}_{d})$$ ( F d ) , lithology, normalized difference vegetation index (NDVI), plan curvature $${(\mathrm{Pl}}_{\mathrm{c}})$$ ( Pl c ) , profile curvature $${(\mathrm{Pr}}_{\mathrm{c}})$$ ( Pr c ) , slope $${(S}^{^\circ })$$ ( S ∘ ) , stream power index (SPI), and topographic wetness index (TWI). The multi-collinearity analysis and mean decrease Gini (MDG) were used to assess the suitability and predictability of these factors. Consequently, five landslide susceptibility prediction (LSP) maps were generated and validated using accuracy, area under the receiver operatic characteristic curve, sensitivity, and specificity. The MDG results demonstrated that the rainfall, elevation, and lithology were the most significant landslide conditioning factors ruling the occurrence of landslides in Qinghai-Tibetan Plateau. The LSP maps depicted that the north-northwestern and south-southeastern regions ( 45% of total area). Moreover, among the five models with a high goodness-of-fit, RF model was highlighted as the superior one, by which higher accuracy of landslide susceptibility assessment and better prone areas management in QTP can be achieved compared to previous results. Graphical Abstrac

Landslide Susceptibility Mapping Using Machine Learning:A Danish Case Study

Mapping of landslides, conducted in 2021 by the Geological Survey of Denmark and Greenland (GEUS), revealed 3202 landslides in Denmark, indicating that they might pose a bigger problem than previously acknowledged. Moreover, the changing climate is assumed to have an impact on landslide occurrences in the future. The aim of this study is to conduct the first landslide susceptibility mapping (LSM) in Denmark, reducing the geographical bias existing in LSM studies, and to identify areas prone to landslides in the future following representative concentration pathway RCP8.5, based on a set of explanatory variables in an area of interest located around Vejle Fjord, Jutland, Denmark. A subset from the landslide inventory provided by GEUS is used as ground truth data. Three well-established machine learning (ML) algorithms—Random Forest, Support Vector Machine, and Logistic Regression—were trained to classify the data samples as landslide or non-landslide, treating the ML task as a binary classification and expressing the results in the form of a probability in order to produce susceptibility maps. The classification results were validated through the test data and through an external data set for an area located outside of the region of interest. While the high predictive performance varied slightly among the three models on the test data, the LR and SVM demonstrated inferior accuracy outside of the study area. The results show that the RF model has robustness and potential for applicability in landslide susceptibility mapping in low-lying landscapes of Denmark in the present. The conducted mapping can become a step forward towards planning for mitigative and protective measures in landslide-prone areas in Denmark, providing policy-makers with necessary decision support. However, the map of the future climate change scenario shows the reduction of the susceptible areas, raising the question of the choice of the climate models and variables in the analysis

Landslide Susceptibility Prediction Modeling Based on Self-Screening Deep Learning Model

Landslide susceptibility prediction has always been an important and challenging content. However, there are some uncertain problems to be solved in susceptibility modeling, such as the error of landslide samples and the complex nonlinear relationship between environmental factors. A self-screening graph convolutional network and long short-term memory network (SGCN-LSTM) is proposed int this paper to overcome the above problems in landslide susceptibility prediction. The SGCN-LSTM model has the advantages of wide width and good learning ability. The landslide samples with large errors outside the set threshold interval are eliminated by self-screening network, and the nonlinear relationship between environmental factors can be extracted from both spatial nodes and time series, so as to better simulate the nonlinear relationship between environmental factors. The SGCN-LSTM model was applied to landslide susceptibility prediction in Anyuan County, Jiangxi Province, China, and compared with Cascade-parallel Long Short-Term Memory and Conditional Random Fields (CPLSTM-CRF), Random Forest (RF), Support Vector Machine (SVM), Stochastic Gradient Descent (SGD) and Logistic Regression (LR) models.The landslide prediction experiment in Anyuan County showed that the total accuracy and AUC of SGCN-LSTM model were the highest among the six models, and the total accuracy reached 92.38 %, which was 5.88%, 12.44%, 19.65%, 19.92% and 20.34% higher than those of CPLSTM-CRF, RF, SVM, SGD and LR models, respectively. The AUC value reached 0.9782, which was 0.0305,0.0532,0.1875,0.1909 and 0.1829 higher than the other five models, respectively. In conclusion, compared with some existing traditional machine learning, the SGCN-LSTM model proposed in this paper has higher landslide prediction accuracy and better robustness, and has a good application prospect in the LSP field

Advancements, Challenges, and Future Directions in Rainfall-Induced Landslide Prediction: A Comprehensive Review

Rainfall-induced landslides threaten lives and properties globally. To address this, researchers have developed various methods and models that forecast the likelihood and behavior of rainfall-induced landslides. These methodologies and models can be broadly classified into three categories: empirical, physical-based, and machine-learning approaches. However, these methods have limitations in terms of data availability, accuracy, and applicability. This paper reviews the current state-of-the-art of rainfall-induced landslide prediction methods, focusing on the methods, models, and challenges involved. The novelty of this study lies in its comprehensive analysis of existing prediction techniques and the identification of their limitations. By synthesizing a vast body of research, it highlights emerging trends and advancements, providing a holistic perspective on the subject matter. The analysis points out that future research opportunities lie in interdisciplinary collaborations, advanced data integration, remote sensing, climate change impact analysis, numerical modeling, real-time monitoring, and machine learning improvements. In conclusion, the prediction of rainfall-induced landslides is a complex and multifaceted challenge, and no single approach is universally superior. Integrating different methods and leveraging emerging technologies offer the best way forward for improving accuracy and reliability in landslide prediction, ultimately enhancing our ability to manage and mitigate this geohazard

Advancements, Challenges, and Future Directions in Rainfall-Induced Landslide Prediction: A Comprehensive Review

Rainfall-induced landslides threaten lives and properties globally. To address this, researchers have developed various methods and models that forecast the likelihood and behavior of rainfall-induced landslides. These methodologies and models can be broadly classified into three categories: empirical, physical-based, and machine-learning approaches. However, these methods have limitations in terms of data availability, accuracy, and applicability. This paper reviews the current state-of-the-art of rainfall-induced landslide prediction methods, focusing on the methods, models, and challenges involved. The novelty of this study lies in its comprehensive analysis of existing prediction techniques and the identification of their limitations. By synthesizing a vast body of research, it highlights emerging trends and advancements, providing a holistic perspective on the subject matter. The analysis points out that future research opportunities lie in interdisciplinary collaborations, advanced data integration, remote sensing, climate change impact analysis, numerical modeling, real-time monitoring, and machine learning improvements. In conclusion, the prediction of rainfall-induced landslides is a complex and multifaceted challenge, and no single approach is universally superior. Integrating different methods and leveraging emerging technologies offer the best way forward for improving accuracy and reliability in landslide prediction, ultimately enhancing our ability to manage and mitigate this geohazard

A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides

This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas