Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster

We develop a stochastic modeling approach based on spatial point processes of log-Gaussian Cox type for a collection of around 5000 landslide events provoked by a precipitation trigger in Sicily, Italy. Through the embedding into a hierarchical Bayesian estimation framework, we can use the Integrated Nested Laplace Approximation methodology to make inference and obtain the posterior estimates. Several mapping units are useful to partition a given study area in landslide prediction studies. These units hierarchically subdivide the geographic space from the highest grid-based resolution to the stronger morphodynamic-oriented slope units. Here we integrate both mapping units into a single hierarchical model, by treating the landslide triggering locations as a random point pattern. This approach diverges fundamentally from the unanimously used presence-absence structure for areal units since we focus on modeling the expected landslide count jointly within the two mapping units. Predicting this landslide intensity provides more detailed and complete information as compared to the classically used susceptibility mapping approach based on relative probabilities. To illustrate the model's versatility, we compute absolute probability maps of landslide occurrences and check its predictive power over space. While the landslide community typically produces spatial predictive models for landslides only in the sense that covariates are spatially distributed, no actual spatial dependence has been explicitly integrated so far for landslide susceptibility. Our novel approach features a spatial latent effect defined at the slope unit level, allowing us to assess the spatial influence that remains unexplained by the covariates in the model

Incorporating Rainfall Forecast Data in X-SLIP Platform to Predict the Triggering of Rainfall-Induced Shallow Landslides in Real Time

Extreme and prolonged rainfall resulting from global warming determines a growing need for reliable Landslide Early Warning Systems (LEWS) to manage the risk of rainfall-induced shallow landslides (also called soil slips). Regional LEWS are typically based on data-driven methods because of their greater computational effectiveness, which is greater than the ones of physically based models (PBMs); however, the latter reproduces the physical mechanism of the modelled phenomena, and their modelling is more accurate. The purpose of this research is to investigate the prediction quality of the simplified PBM SLIP (implemented in the X-SLIP platform) when applied on a regional scale by analysing the stability of rain forecasts. X-SLIP was updated to handle the GRIB files (format for weather forecast). Four real-time predictions were simulated on some towns of the Emilia Apennines (northern Italy) involved in widespread soil slips on 5 April 2013; specifically, maps of factors of safety related to this event were derived assuming that X-SLIP had run 72 h, 48 h, 24 h and 12 h in advance. The results indicated that the predictions with forecasts (depending on the forecast quality) are as accurate as the ones derived with rainfall recordings only (benchmark). Moreover, the proposed method provides a reduced number of false alarms when no landslide was reported to occur in the whole area. X-SLIP with rain forecasts can, therefore, represent an important tool to predict the occurrence of future soil slips at a regional scale

Climate Change Impact Assessment for Surface Transportation in the Pacific Northwest and Alaska

WA-RD 772.

Soil erosion in the Alps : causes and risk assessment

The issue of soil erosion in the Alps has long been neglected due to the low economic value of the agricultural land. However, soil stability is a key parameter which affects ecosystem services like slope stability, water budgets (drinking water reservoirs as well as flood prevention), vegetation productivity, ecosystem biodiversity and nutrient production. In alpine regions, spatial estimates on soil erosion are difficult to derive because the highly heterogeneous biogeophysical structure impedes measurement of soil erosion and the applicability of soil erosion models. However, remote sensing and geographic information system (GIS) methods allow for spatial estimation of soil erosion by direct detection of erosion features and supply of input data for soil erosion models. Thus, the main objective of this work is to address the problem of soil erosion risk assessment in the Alps on catchment scale with remote sensing and GIS tools. Regarding soil erosion processes the focus is on soil erosion by water (here sheet erosion) and gravity (here landslides). For these two processes we address i) the monitoring and mapping of the erosion features and related causal factors ii) soil erosion risk assessment with special emphasis on iii) the validation of existing models for alpine areas. All investigations were accomplished in the Urseren Valley (Central Swiss Alps) where the valley slopes are dramatically affected by sheet erosion and landslides. For landslides, a natural susceptibility of the catchment has been indicated by bivariate and multivariate statistical analysis. Geology, slope and stream density are the most significant static landslide causal factors. Static factors are here defined as factors that do not change their attributes during the considered time span of the study (45 years), e.g. geology, stream network. The occurrence of landslides might be significantly increased by the combined effects of global climate and land use change. Thus, our hypothesis is that more recent changes in land use and climate affected the spatial and temporal occurrence of landslides. The increase of the landslide area of 92% within 45 years in the study site confirmed our hypothesis. In order to identify the cause for the trend in landslide occurrence time-series of landslide causal factors were analysed. The analysis revealed increasing trends in the frequency and intensity of extreme rainfall events and stocking of pasture animals. These developments presumably enhanced landslide hazard. Moreover, changes in land-cover and land use were shown to have affected landslide occurrence. For instance, abandoned areas and areas with recently emerging shrub vegetation show very low landslide densities. Detailed spatial analysis of the land use with GIS and interviews with farmers confirmed the strong influence of the land use management practises on slope stability. The definite identification and quantification of the impact of these non-stationary landslide causal factors (dynamic factors) on the landslide trend was not possible due to the simultaneous change of several factors. The consideration of dynamic factors in statistical landslide susceptibility assessments is still unsolved. The latter may lead to erroneous model predictions, especially in times of dramatic environmental change. Thus, we evaluated the effect of dynamic landslide causal factors on the validity of landslide susceptibility maps for spatial and temporal predictions. For this purpose, a logistic regression model based on data of the year 2000 was set up. The resulting landslide susceptibility map was valid for spatial predictions. However, the model failed to predict the landslides that occurred in a subsequent event. In order to handle this weakness of statistic landslide modelling a multitemporal approach was developed. It is based on establishing logistic regression models for two points in time (here 1959 and 2000). Both models could correctly classify >70% of the independent spatial validation dataset. By subtracting the 1959 susceptibility map from the 2000 susceptibility map a deviation susceptibility map was obtained. Our interpretation was that these susceptibility deviations indicate the effect of dynamic causal factors on the landslide probability. The deviation map explained 85% of new independent landslides occurring after 2000. Thus, we believe it to be a suitable tool to add a time element to a susceptibility map pointing to areas with changing susceptibility due to recently changing environmental conditions or human interactions. In contrast to landslides that are a direct threat to buildings and infrastructure, sheet erosion attracts less attention because it is often an unseen process. Nonetheless, sheet erosion may account for a major proportion of soil loss. Soil loss by sheet erosion is related to high spatial variability, however, in contrast to arable fields for alpine grasslands erosion damages are long lasting and visible over longer time periods. A crucial erosion triggering parameter that can be derived from satellite imagery is fractional vegetation cover (FVC). Measurements of the radiogenic isotope Cs-137, which is a common tracer for soil erosion, confirm the importance of FVC for soil erosion yield in alpine areas. Linear spectral unmixing (LSU), mixture tuned matched filtering (MTMF) and the spectral index NDVI are applied for estimating fractional abundance of vegetation and bare soil. To account for the small scale heterogeneity of the alpine landscape very high resolved multispectral QuickBird imagery is used. The performance of LSU and MTMF for estimating percent vegetation cover is good (r²=0.85, r²=0.71 respectively). A poorer performance is achieved for bare soil (r²=0.28, r²=0.39 respectively) because compared to vegetation, bare soil has a less characteristic spectral signature in the wavelength domain detected by the QuickBird sensor. Apart from monitoring erosion controlling factors, quantification of soil erosion by applying soil erosion risk models is done. The performance of the two established models Universal Soil Loss Equation (USLE) and Pan-European Soil Erosion Risk Assessment (PESERA) for their suitability to model erosion for mountain environments is tested. Cs-137 is used to verify the resulting erosion rates from USLE and PESERA. PESERA yields no correlation to measured Cs-137 long term erosion rates and shows lower sensitivity to FVC. Thus, USLE is used to model the entire study site. The LSU-derived FVC map is used to adapt the C factor of the USLE. Compared to the low erosion rates computed with the former available low resolution dataset (1:25000) the satellite supported USLE map shows “hotspots” of soil erosion of up to 16 t ha-1 a-1. In general, Cs-137 in combination with the USLE is a very suitable method to assess soil erosion for larger areas, as both give estimates on long-term soil erosion. Especially for inaccessible alpine areas, GIS and remote sensing proved to be powerful tools that can be used for repetitive measurements of erosion features and causal factors. In times of global change it is of crucial importance to account for temporal developments. However, the evaluation of the applied soil erosion risk models revealed that the implementation of temporal aspects, such as varying climate, land use and vegetation cover is still insufficient. Thus, the proposed validation strategies (spatial, temporal and via Cs-137) are essential. Further case studies in alpine regions are needed to test the methods elaborated for the Urseren Valley. However, the presented approaches are promising with respect to improve the monitoring and identification of soil erosion risk areas in alpine regions

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

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 displacement forecasting using deep learning and monitoring data across selected sites

Accurate early warning systems for landslides are a reliable risk-reduction strategy that may significantly reduce fatalities and economic losses. Several machine learning methods have been examined for this purpose, underlying deep learning (DL) models’ remarkable prediction capabilities. The long short-term memory (LSTM) and gated recurrent unit (GRU) algorithms are the sole DL model studied in the extant comparisons. However, several other DL algorithms are suitable for time series forecasting tasks. In this paper, we assess, compare, and describe seven DL methods for forecasting future landslide displacement: multi-layer perception (MLP), LSTM, GRU, 1D convolutional neural network (1D CNN), 2xLSTM, bidirectional LSTM (bi-LSTM), and an architecture composed of 1D CNN and LSTM (Conv-LSTM). The investigation focuses on four landslides with different geographic locations, geological settings, time step dimensions, and measurement instruments. Two landslides are located in an artificial reservoir context, while the displacement of the other two is influenced just by rainfall. The results reveal that the MLP, GRU, and LSTM models can make reliable predictions in all four scenarios, while the Conv- LSTM model outperforms the others in the Baishuihe landslide, where the landslide is highly seasonal. No evident performance differences were found for landslides inside artificial reservoirs rather than outside. Furthermore, the research shows that MLP is better adapted to forecast the highest displacement peaks, while LSTM and GRU are better suited to model lower displacement peaks. We believe the findings of this research will serve as a precious aid when implementing a DL-based landslide early warning system (LEWS).SUPPORTO SCIENTIFICO PER L’OTTIMIZZAZIONE, IMPLEMENTAZIONE E GESTIONE DEL SISTEMA DI MONITORAGGIO CON AGGIORNAMENTO DELLE SOGLIE DI ALLERTAMENTO DEL FENOMENO FRANOSO DI SANT’ANDREA – PERAROLO DI CADORE (BL)” and the Spanish Grant “SARAI, PID2020-116540RB-C21,MCIN/AEI/10.13039/501100011033” and “RISKCOASTInSAR displacement data of the El Arrecife landslideGeohazard Exploitation Platform (GEP) of the European Space AgencyNoR Projects Sponsorship (Project ID: 63737