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

An Application of Artificial Neural Network (ANN) for Landslide Hazard Mapping, Susceptibility and Early Warning System: A Review

An Application of ANN for Landslide Early Warning System in Darjeeling hill regio

Landslide susceptibility mapping on the islands of Vulcano and Lipari (Aeolian Archipelago, Italy), using a multi-classification approach on conditioning factors and a modified GIS matrix method for areas lacking in a landslide inventory

The final publication is available at (la publicación final está disponible en): https://link.springer.com/article/10.1007/s10346-019-01148-0#citeas https://rdcu.be/duoifIn areas prone to landslides, the identification of potentially unstable zones has a decisive impact on the risk assessment and development of mitigation plans. Active volcanic islands are particularly prone to instability phenomena as they are always in the early stage of dynamic unrest. A historical example of slope instability is the landslide which occurred in 1988 along the northwestern flank of La Fossa Cone on the island of Vulcano (Aeolian Archipelago). Based on this past activity, a susceptibility assessment using the bivariate technique of the GIS matrix method (GMM) was carried out on the islands of Lipari and Vulcano. Nevertheless, this case is congruent with those where a part of the surface was not assigned to stable or unstable areas, since a comprehensive inventory was only available for the island of Lipari. Some of the implemented steps of the susceptibility matrix method were modified to enable the model developed in the Lipari area to be applied to both islands. Considering the important role that the classification of conditioning factors plays in susceptibility analysis, the degree of association with landslide spatial distribution for the multiple classifications of each factor was assessed. Furthermore, an innovative clustering approach based on text and data mining techniques (self-organizing map neural network) was applied and compared with a heuristic classification of the categorical variable of lithology units. In addition to the extensive contingency analysis, up to 14 factor combinations were submitted to the GMM, validated and compared so as to select the one that best explains the susceptibility zoning. The effects of these incorporated processes in the previous phase of classification were discussed and reliminary susceptibility map was generated for both islands. After the validation of the susceptibility assessment, it is shown that the highest classes (High and Very High) matched 76.9% (relative accuracy) of the test inventory, while the lower susceptibility classes (Very Low and Low) resulted in a degree of fit of 14.39% (relative error).This work was supported by the DPC-INGV Project V3 on the island of Vulcano (http://sites.google.com/site/progettivulcanologici), founded by the Italian National Institute of Geophysics and Volcanology and by the Italian Civil Protection Department. The 2008 ALS DTM was provided by the Italian Ministry for Environment.This work has been supported by the RNM121 Group of the Andalusian Regional Government

Landslide prediction using artificial neural networks

Landslides are the most recurrent and prominent disaster in Sri Lanka. Sri Lanka has been subjected to a number of extreme landslide disasters that resulted in a significant loss of life, material damage, and distress. It is required to explore a solution towards preparedness and mitigation to reduce recurrent losses associated with landslides. Artificial Neural Networks (ANNs) are now widely used in many computer applications spanning multiple domains. This research examines the effectiveness of using Artificial Neural Networks in landslides predictions and the possibility of applying the modern technology to predict landslides in a prominent geographical area in Sri Lanka. A thorough survey was conducted with the participation of resource persons from several national universities in Sri Lanka to identify and rank the influencing factors for landslides. A landslide database was created using existing topographic; soil, drainage, land cover maps and historical data. The landslide related factors which include external factors (Rainfall, Number of Previous Occurrences and Influence of Construction) and internal factors (Soil Material, Geology, Land Use, Curvature, Soil Texture, Slope, Aspect, Soil Drainage, and Soil Effective Thickness) are extracted from the landslide database. These factors are used to recognize the possibility to occur landslides by using an ANN. The network acquires the relationship between the factors of landslide and its hazard index during the training session. This model with landslide related factors as the inputs will be trained to predict three classes namely, „landslide occurs‟, „landslide does not occur‟ and „landslide likely to occur‟. Once trained, the model will be able to predict the most likely class for the prevailing data. Experiments show a high prediction rate with 83% accuracy. This research indicates that the proposed mechanism could be used as a strong decision support system to predict landslides efficiently and effectively

Landslide detection by deep learning of non-nadiral and crowdsourced optical images

The recent development of mobile surveying platforms and crowdsourced geoinformation has produced a huge amount of non-validated data that are now available for research and application. In the field of risk analysis, with particular reference to landslide hazard, images generated by autonomous platforms (such as UAVs, ground-based acquisition systems, satellite sensors) and pictures obtained from web data mining are easily gathered and contribute to the fast surge in the amount of non-organized information that may engulf data storage facilities. Therefore, the high potential impact of such methods is severely reduced by the need of a massive amount of human intelligence tasks (HITs), which is necessary to filter and classify the data, whatever the final purpose. In this work, we present a new set of convolutional neural networks (CNNs) specifically designed for the automated recognition of landslides and mass movements in non-standard pictures that can be used in automated image classification, in supporting UAV autonomous guidance and in the filtering of data-mined information. Computer vision can be of great help in fostering the autonomous capability of intelligent systems to complement, or completely substitute, HITs. Image and object recognition are at the forefront of this research field. The deep learning procedure has been accomplished by applying transfer learning to some of the top-performer CNNs available in the literature. Results show that the deep learning machines, calibrated on a relevant dataset of validated images of landforms, may supply reliable predictions with computational time and resource requirements compatible with most of the UAV platforms and web data mining applications in landslide hazard studies. Average accuracy achieved by the proposed methods ranges between 87 and 90% and is consistently higher than that obtained by general-purpose state-of-the-art image recognition convolutional neural networks. The method can be applied to early warning, vulnerability assessment, residual risk estimation, model parameterisation and landslide mapping. Specific advantages will be the reduction of the present limitations in the intelligent guidance of landslide mapping drones, the classification of fake news, the validation of post-disaster information and the correct interpretation of an impending change in the environment

Riverside Landslide Susceptibility Overview: Leveraging Artificial Neural Networks and Machine Learning in Accordance with the United Nations (UN) Sustainable Development Goals

Riverside landslides present a significant geohazard globally, posing threats to infrastructure and human lives. In line with the United Nations’ Sustainable Development Goals (SDGs), which aim to address global challenges, professionals in the field have developed diverse methodologies to analyze, assess, and predict the occurrence of landslides, including quantitative, qualitative, and semi-quantitative approaches. With the advent of computer programs, quantitative techniques have gained prominence, with computational intelligence and knowledge-based methods like artificial neural networks (ANNs) achieving remarkable success in landslide susceptibility assessments. This article offers a comprehensive review of the literature concerning the utilization of ANNs for landslide susceptibility assessment, focusing specifically on riverside areas, in alignment with the SDGs. Through a systematic search and analysis of various references, it has become evident that ANNs have emerged as the preferred method for these assessments, surpassing traditional approaches. The application of ANNs aligns with the SDGs, particularly Goal 11: Sustainable Cities and Communities, which emphasizes the importance of inclusive, safe, resilient, and sustainable urban environments. By effectively assessing riverside landslide susceptibility using ANNs, communities can better manage risks and enhance the resilience of cities and communities to geohazards. While the number of ANN-based studies in landslide susceptibility modeling has grown in recent years, the overarching objective remains consistent: researchers strive to develop more accurate and detailed procedures. By leveraging the power of ANNs and incorporating relevant SDGs, this survey focuses on the most commonly employed neural network methods for riverside landslide susceptibility mapping, contributing to the overall SDG agenda of promoting sustainable development, resilience, and disaster risk reduction. Through the integration of ANNs in riverside landslide susceptibility assessments, in line with the SDGs, this review aims to advance our knowledge and understanding of this field. By providing insights into the effectiveness of ANNs and their alignment with the SDGs, this research contributes to the development of improved risk management strategies, sustainable urban planning, and resilient communities in the face of riverside landslides

Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models

Spatial susceptible landslide prediction is the one of the most challenging research areas which essentially concerns the safety of inhabitants. The novel geographic information web (GIW) application is proposed for dynamically predicting landslide risk in Chiang Rai, Thailand. The automated GIW system is coordinated between machine learning technologies, web technologies, and application programming interfaces (APIs). The new bidirectional long short-term memory (Bi-LSTM) algorithm is presented to forecast landslides. The proposed algorithm consists of 3 major steps, the first of which is the construction of a landslide dataset by using Quantum GIS (QGIS). The second step is to generate the landslide-risk model based on machine learning approaches. Finally, the automated landslide-risk visualization illustrates the likelihood of landslide via Google Maps on the website. Four static factors are considered for landslide-risk prediction, namely, land cover, soil properties, elevation and slope, and a single dynamic factor i.e., precipitation. Data are collected to construct a geospatial landslide database which comprises three historical landslide locations—Phu Chifa at Thoeng District, Ban Pha Duea at Mae Salong Nai, and Mai Salong Nok in Mae Fa Luang District, Chiang Rai, Thailand. Data collection is achieved using QGIS software to interpolate contour, elevation, slope degree and land cover from the Google satellite images, aerial and site survey photographs while the physiographic and rock type are on-site surveyed by experts. The state-of-the-art machine learning models have been trained i.e., linear regression (LR), artificial neural network (ANN), LSTM, and Bi-LSTM. Ablation studies have been conducted to determine the optimal parameters setting for each model. An enhancement method based on two-stage classifications has been presented to improve the landslide prediction of LSTM and Bi-LSTM models. The landslide-risk prediction performances of these models are subsequently evaluated using real-time dataset and it is shown that Bi-LSTM with Random Forest (Bi-LSTM-RF) yields the best prediction performance. Bi-LSTM-RF model has improved the landslide-risk predicting performance over LR, ANNs, LSTM, and Bi-LSTM in terms of the area under the receiver characteristic operator (AUC) scores by 0.42, 0.27, 0.46, and 0.47, respectively. Finally, an automated web GIS has been developed and it consists of software components including the trained models, rainfall API, Google API, and geodatabase. All components have been interfaced together via JavaScript and Node.js tool

A novel hybrid swarm optimized multilayer neural network for spatial prediction of flash floods in tropical areas using sentinel-1 SAR imagery and geospatial data

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg–Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility