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Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".

Deadline for manuscript submissions: closed (10 April 2023) | Viewed by 29153

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Special Issue Editors

Dr. Junwei Ma

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Guest Editor

Badong National Observation and Research Station of Geohazards (BNORSG), China University of Geosciences, Wuhan, China
Interests: geohazard prediction; machine learning

Prof. Dr. Jie Dou

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Guest Editor

Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China
Interests: artificial intelligence; geohazards; geospatial analysis; geomorphology; risk mitigation
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Special Issue Information

Dear Colleagues,

Geohazards, such as landslides, rock avalanches, debris flow, ground fissures, and ground subsidence, pose significant threats to life and property. Recently, machine learning (ML) has become the predominant approach in geohazard modeling due to its advantages of providing excellent generalization ability and accurately describing complex and nonlinear behavior.

However, the use of advanced algorithms in deep learning is still poorly understood in this field. Further, there are fundamental challenges associated with ML modeling, including input variable selection, uncertainty quantification, and hyperparameter tuning.

This Special Issue will provide a forum for original research work exploring new frontiers and challenges for the applications of ML for spatial-temporal modeling of geohazards. Moreover, innovative methods and original applications (including but not limited to geohazard prediction, recognition, early warning systems, susceptibility mapping, risk management, spatial modeling, and mitigation measurement) would be appropriate.

The topics of interest include but are not limited to:

Geohazard modeling;
Spatial–temporal prediction;
Machine learning;
Deep and reinforcement learning;
Metaheuristic optimized machine learning approach;
Physics-based and data-driven hybrid modeling.

Dr. Junwei Ma
Prof. Dr. Jie Dou
Guest Editors

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Published Papers (12 papers)

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Editorial

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4 pages, 176 KiB

Open AccessEditorial

Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard

by Junwei Ma and Jie Dou

Sensors 2023, 23(22), 9262; https://0-doi-org.brum.beds.ac.uk/10.3390/s23229262 - 18 Nov 2023

Viewed by 841

Abstract

Geohazards, such as landslides, rock avalanches, debris flow, ground fissures, and ground subsidence, pose significant threats to people’s lives and property [...] Full article

(This article belongs to the Special Issue Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard)

Research

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21 pages, 12417 KiB

Open AccessArticle

Susceptibility Analysis of Glacier Debris Flow Based on Remote Sensing Imagery and Deep Learning: A Case Study along the G318 Linzhi Section

by Jiaqing Chen, Hong Gao, Le Han, Ruilin Yu and Gang Mei

Sensors 2023, 23(14), 6608; https://0-doi-org.brum.beds.ac.uk/10.3390/s23146608 - 22 Jul 2023

Cited by 1 | Viewed by 1361

Abstract

Glacial debris flow is a common natural disaster, and its frequency has been increasing in recent years due to the continuous retreat of glaciers caused by global warming. To reduce the damage caused by glacial debris flows to human and physical properties, glacier susceptibility assessment analysis is needed. Most research efforts consider the effect of existing glacier area and ignore the effect of glacier ablation volume change. In this paper, we consider the impact of glacier ablation volume change to investigate the susceptibility of glacial debris flow. The susceptibility to mudslide was evaluated by taking the glacial mudslide-prone ditch of G318 Linzhi section of Sichuan-Tibet Highway as the research object. First, by using a simple band ratio method with manual correction, we produced a glacial mudslide remote sensing image dataset, and second, we proposed a deep-learning-based approach using a weight-optimized glacial mudslide semantic segmentation model for accurately and automatically mapping the boundaries of complex glacial mudslide-covered remote sensing images. Then, we calculated the ablation volume by the change in glacier elevation and ablation area from 2015 to 2020. Finally, glacial debris flow susceptibility was evaluated based on the entropy weight method and Topsis method with glacial melt volume in different watersheds as the main factor. The research results of this paper show that most of the evaluation indices of the model are above 90%, indicating that the model is reasonable for glacier boundary extraction, and remote sensing images and deep learning techniques can effectively assess the glacial debris flow susceptibility and provide support for future glacial debris flow disaster prevention. Full article

(This article belongs to the Special Issue Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard)

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29 pages, 5140 KiB

Open AccessArticle

Machine Learning Models for Slope Stability Classification of Circular Mode Failure: An Updated Database and Automated Machine Learning (AutoML) Approach

by Junwei Ma, Sheng Jiang, Zhiyang Liu, Zhiyuan Ren, Dongze Lei, Chunhai Tan and Haixiang Guo

Sensors 2022, 22(23), 9166; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239166 - 25 Nov 2022

Cited by 13 | Viewed by 2177

Abstract

Slope failures lead to large casualties and catastrophic societal and economic consequences, thus potentially threatening access to sustainable development. Slope stability assessment, offering potential long-term benefits for sustainable development, remains a challenge for the practitioner and researcher. In this study, for the first time, an automated machine learning (AutoML) approach was proposed for model development and slope stability assessments of circular mode failure. An updated database with 627 cases consisting of the unit weight, cohesion, and friction angle of the slope materials; slope angle and height; pore pressure ratio; and corresponding stability status has been established. The stacked ensemble of the best 1000 models was automatically selected as the top model from 8208 trained models using the H2O-AutoML platform, which requires little expert knowledge or manual tuning. The top-performing model outperformed the traditional manually tuned and metaheuristic-optimized models, with an area under the receiver operating characteristic curve (AUC) of 0.970 and accuracy (ACC) of 0.904 based on the testing dataset and achieving a maximum lift of 2.1. The results clearly indicate that AutoML can provide an effective automated solution for machine learning (ML) model development and slope stability classification of circular mode failure based on extensive combinations of algorithm selection and hyperparameter tuning (CASHs), thereby reducing human efforts in model development. The proposed AutoML approach has the potential for short-term severity mitigation of geohazard and achieving long-term sustainable development goals. Full article

(This article belongs to the Special Issue Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard)

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19 pages, 3998 KiB

Open AccessArticle

Capture and Prediction of Rainfall-Induced Landslide Warning Signals Using an Attention-Based Temporal Convolutional Neural Network and Entropy Weight Methods

by Di Zhang, Kai Wei, Yi Yao, Jiacheng Yang, Guolong Zheng and Qing Li

Sensors 2022, 22(16), 6240; https://0-doi-org.brum.beds.ac.uk/10.3390/s22166240 - 19 Aug 2022

Cited by 6 | Viewed by 1608

Abstract

The capture and prediction of rainfall-induced landslide warning signals is the premise for the implementation of landslide warning measures. An attention-fusion entropy weight method (En-Attn) for capturing warning features is proposed. An attention-based temporal convolutional neural network (ATCN) is used to predict the warning signals. Specifically, the sensor data are analyzed using Pearson correlation analysis after obtaining data from the sensors on rainfall, moisture content, displacement, and soil stress. The comprehensive evaluation score is obtained offline using multiple entropy weight methods. Then, the attention mechanism is used to weight and sum different entropy values to obtain the final landslide hazard degree (LHD). The LHD realizes the warning signal capture of the sensor data. The prediction process adopts a model built by ATCN and uses a sliding window for online dynamic prediction. The input is the landslide sensor data at the last moment, and the output is the LHD at the future moment. The effectiveness of the method is verified by two datasets obtained from the rainfall-induced landslide simulation experiment. Full article

(This article belongs to the Special Issue Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard)

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26 pages, 20106 KiB

Open AccessArticle

Landslide Susceptibility Mapping Using Machine Learning Algorithm Validated by Persistent Scatterer In-SAR Technique

by Muhammad Afaq Hussain, Zhanlong Chen, Ying Zheng, Muhammad Shoaib, Safeer Ullah Shah, Nafees Ali and Zeeshan Afzal

Sensors 2022, 22(9), 3119; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093119 - 19 Apr 2022

Cited by 36 | Viewed by 3975

Abstract

Landslides are the most catastrophic geological hazard in hilly areas. The present work intends to identify landslide susceptibility along Karakorum Highway (KKH) in Northern Pakistan, using landslide susceptibility mapping (LSM). To compare and predict the connection between causative factors and landslides, the random forest (RF), extreme gradient boosting (XGBoost), k nearest neighbor (KNN) and naive Bayes (NB) models were used in this research. Interferometric synthetic aperture radar persistent scatterer interferometry (PS-InSAR) technology was used to explore the displacement movement of retrieved models. Initially, 332 landslide areas alongside the Karakorum Highway were found to generate the landslide inventory map using various data. The landslides were categorized into two sections for validation and training, of 30% and 70%. For susceptibility mapping, thirteen landslide-condition factors were created. The area under curve (AUC) of the receiver operating characteristic (ROC) curve technique was utilized for accuracy comparison, yielding 83.08, 82.15, 80.31, and 72.92% accuracy for RF, XGBoost, KNN, and NB, respectively. The PS-InSAR technique demonstrated a high deformation velocity along the line of sight (LOS) in model-sensitive areas. The PS-InSAR technique was used to evaluate the slope deformation velocity, which can be used to improve the LSM for the research region. The RF technique yielded superior findings, integrating with the PS-InSAR outcomes to provide the region with a new landslide susceptibility map. The enhanced model will help mitigate landslide catastrophes, and the outcomes may help ensure the roadway’s safe functioning in the study region. Full article

(This article belongs to the Special Issue Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard)

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22 pages, 7182 KiB

Open AccessArticle

Slope with Predetermined Shear Plane Stability Predictions under Cyclic Loading with Innovative Time Series Analysis by Mechanical Learning Approach

by Tingyao Wu, Hongan Yu, Nan Jiang, Chuanbo Zhou and Xuedong Luo

Sensors 2022, 22(7), 2647; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072647 - 30 Mar 2022

Viewed by 1711

Abstract

We propose a mechanical learning method that can be used to predict stability coefficients for slopes where slopes with predetermined shear planes are subjected to cyclic seismic loads under undrained conditions. Firstly, shear tests with cyclic loading of different parameters were simulated on designated slip zone soil specimens, in which the strain softening process leading to landslide occurrence was closely observed. At the same time, based on the limit equilibrium analysis of the Sarma method, the variation of slope stability coefficients under different cyclic loads was investigated. Finally, a Box–Jenkins’ modeling approach is used to predict the data from the time series of slope stability coefficients using a mechanical learning approach. The simulation results show that (1) reduction in coordination number can be an accurate indicator of the level of strain softening and evolutionary processes; (2) the gradual reduction of shear stress facilitates the soil strain softening process, while different cyclic loading stress amplitudes will result in rapid penetration or non-penetration of the fracture zone by means of particulate flow. Although the confining pressure of the slip zone soil can inhibit the increase of fractures, it has a limited inhibitory effect on strain softening; (3) based on field observations of the slope stability factor and stress field, two possible landslide triggering mechanisms are described. (4) Mechanical learning of time series can accurately predict the changing pattern of stability coefficients of slopes without loading. This study establishes a potential bridge between the geological investigation of landslides and the theoretical background of landslide stability coefficient prediction. Full article

(This article belongs to the Special Issue Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard)

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28 pages, 8678 KiB

Open AccessArticle

A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran

by Bahareh Ghasemian, Himan Shahabi, Ataollah Shirzadi, Nadhir Al-Ansari, Abolfazl Jaafari, Victoria R. Kress, Marten Geertsema, Somayeh Renoud and Anuar Ahmad

Sensors 2022, 22(4), 1573; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041573 - 17 Feb 2022

Cited by 31 | Viewed by 3089

Abstract

We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm (GA). A total of 118 landslide locations were recorded and divided in the training and testing datasets. We selected 25 conditioning factors, and of these, we specified the most important ones by an information gain ratio (IGR) technique. We assessed the performance of the DP model using statistical measures including sensitivity, specificity, accuracy, F1-measure, and area under-the-receiver operating characteristic curve (AUC). Three benchmark algorithms, i.e., support vector machine (SVM), REPTree, and NBTree, were used to check the applicability of the proposed model. The results by IGR concluded that of the 25 conditioning factors, only 16 factors were important for our modeling procedure, and of these, distance to road, road density, lithology and land use were the four most significant factors. Results based on the testing dataset revealed that the DP model had the highest accuracy (0.926) of the compared algorithms, followed by NBTree (0.917), REPTree (0.903), and SVM (0.894). The landslide susceptibility maps prepared from the DP model with AUC = 0.870 performed the best. We consider the DP model a suitable tool for landslide susceptibility mapping. Full article

(This article belongs to the Special Issue Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard)

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21 pages, 8609 KiB

Open AccessArticle

Using Complementary Ensemble Empirical Mode Decomposition and Gated Recurrent Unit to Predict Landslide Displacements in Dam Reservoir

by Beibei Yang, Ting Xiao, Luqi Wang and Wei Huang

Sensors 2022, 22(4), 1320; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041320 - 9 Feb 2022

Cited by 8 | Viewed by 1885

Abstract

It is crucial to predict landslide displacement accurately for establishing a reliable early warning system. Such a requirement is more urgent for landslides in the reservoir area. The main reason is that an inaccurate prediction can lead to riverine disasters and secondary surge disasters. Machine learning (ML) methods have been developed and commonly applied in landslide displacement prediction because of their powerful nonlinear processing ability. Recently, deep ML methods have become popular, as they can deal with more complicated problems than conventional ML methods. However, it is usually not easy to obtain a well-trained deep ML model, as many hyperparameters need to be trained. In this paper, a deep ML method—the gated recurrent unit (GRU)—with the advantages of a powerful prediction ability and fewer hyperparameters, was applied to forecast landslide displacement in the dam reservoir. The accumulated displacement was firstly decomposed into a trend term, a periodic term, and a stochastic term by complementary ensemble empirical mode decomposition (CEEMD). A univariate GRU model and a multivariable GRU model were employed to forecast trend and stochastic displacements, respectively. A multivariable GRU model was applied to predict periodic displacement, and another two popular ML methods—long short-term memory neural networks (LSTM) and random forest (RF)—were used for comparison. Precipitation, reservoir level, and previous displacement were considered to be candidate-triggering factors for inputs of the models. The Baijiabao landslide, located in the Three Gorges Reservoir Area (TGRA), was taken as a case study to test the prediction ability of the model. The results demonstrated that the GRU algorithm provided the most encouraging results. Such a satisfactory prediction accuracy of the GRU algorithm depends on its ability to fully use the historical information while having fewer hyperparameters to train. It is concluded that the proposed model can be a valuable tool for predicting the displacements of landslides in the TGRA and other dam reservoirs. Full article

(This article belongs to the Special Issue Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard)

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17 pages, 14060 KiB

Open AccessArticle

Study on Machine Learning Models for Building Resilience Evaluation in Mountainous Area: A Case Study of Banan District, Chongqing, China

by Chi Zhang, Haijia Wen, Mingyong Liao, Yu Lin, Yang Wu and Hui Zhang

Sensors 2022, 22(3), 1163; https://0-doi-org.brum.beds.ac.uk/10.3390/s22031163 - 3 Feb 2022

Cited by 11 | Viewed by 2589

Abstract

‘Resilience’ is a new concept in the research and application of urban construction. From the perspective of building adaptability in a mountainous environment and maintaining safety performance over time, this paper innovatively proposes machine learning methods for evaluating the resilience of buildings in a mountainous area. Firstly, after considering the comprehensive effects of geographical and geological conditions, meteorological and hydrological factors, environmental factors and building factors, the database of building resilience evaluation models in a mountainous area is constructed. Then, machine learning methods such as random forest and support vector machine are used to complete model training and optimization. Finally, the test data are substituted into models, and the models’ effects are verified by the confusion matrix. The results show the following: (1) Twelve dominant impact factors are screened. (2) Through the screening of dominant factors, the models are comprehensively optimized. (3) The accuracy of the optimization models based on random forest and support vector machine are both 97.4%, and the F1 scores are greater than 94.4%. Resilience has important implications for risk prevention and the control of buildings in a mountainous environment. Full article

(This article belongs to the Special Issue Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard)

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21 pages, 14118 KiB

Open AccessArticle

Data Mining and Deep Learning for Predicting the Displacement of “Step-like” Landslides

by Fasheng Miao, Xiaoxu Xie, Yiping Wu and Fancheng Zhao

Sensors 2022, 22(2), 481; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020481 - 9 Jan 2022

Cited by 11 | Viewed by 2373

Abstract

Landslide displacement prediction is one of the unsolved challenges in the field of geological hazards, especially in reservoir areas. Affected by rainfall and cyclic fluctuations in reservoir water levels, a large number of landslide disasters have developed in the Three Gorges Reservoir Area. In this article, the Baishuihe landslide was taken as the research object. Firstly, based on time series theory, the landslide displacement was decomposed into three parts (trend term, periodic term, and random term) by Variational Mode Decomposition (VMD). Next, the landslide was divided into three deformation states according to the deformation rate. A data mining algorithm was introduced for selecting the triggering factors of periodic displacement, and the Fruit Fly Optimization Algorithm–Back Propagation Neural Network (FOA-BPNN) was applied to the training and prediction of periodic and random displacements. The results show that the displacement monitoring curve of the Baishuihe landslide has a “step-like” trend. Using VMD to decompose the displacement of a landslide can indicate the triggering factors, which has clear physical significance. In the proposed model, the R² values between the measured and predicted displacements of ZG118 and XD01 were 0.977 and 0.978 respectively. Compared with previous studies, the prediction model proposed in this article not only ensures the calculation efficiency but also further improves the accuracy of the prediction results, which could provide guidance for the prediction and prevention of geological disasters. Full article

(This article belongs to the Special Issue Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard)

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28 pages, 9382 KiB

Open AccessArticle

A Novel Model for Landslide Displacement Prediction Based on EDR Selection and Multi-Swarm Intelligence Optimization Algorithm

by Junrong Zhang, Huiming Tang, Dwayne D. Tannant, Chengyuan Lin, Ding Xia, Yankun Wang and Qianyun Wang

Sensors 2021, 21(24), 8352; https://0-doi-org.brum.beds.ac.uk/10.3390/s21248352 - 14 Dec 2021

Cited by 7 | Viewed by 2315

Abstract

With the widespread application of machine learning methods, the continuous improvement of forecast accuracy has become an important task, which is especially crucial for landslide displacement predictions. This study aimed to propose a novel prediction model to improve accuracy in landslide prediction, based on the combination of multiple new algorithms. The proposed new method includes three parts: data preparation, multi-swarm intelligence (MSI) optimization, and displacement prediction. In the data preparation, the complete ensemble empirical mode decomposition (CEEMD) is adopted to separate the trend and periodic displacements from the observed cumulative landslide displacement. The frequency component and residual component of reconstructed inducing factors that related to landslide movements are also extracted by the CEEMD and t-test, and then picked out with edit distance on real sequence (EDR) as input variables for the support vector regression (SVR) model. MSI optimization algorithms are used to optimize the SVR model in the MSI optimization; thus, six predictions models can be obtained that can be used in the displacement prediction part. Finally, the trend and periodic displacements are predicted by six optimized SVR models, respectively. The trend displacement and periodic displacement with the highest prediction accuracy are added and regarded as the final prediction result. The case study of the Shiliushubao landslide shows that the prediction results match the observed data well with an improvement in the aspect of average relative error, which indicates that the proposed model can predict landslide displacements with high precision, even when the displacements are characterized by stepped curves that under the influence of multiple time-varying factors. Full article

(This article belongs to the Special Issue Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard)

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Review

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22 pages, 14432 KiB

Open AccessReview

Scientometric Analysis of Artificial Intelligence (AI) for Geohazard Research

by Sheng Jiang, Junwei Ma, Zhiyang Liu and Haixiang Guo

Sensors 2022, 22(20), 7814; https://0-doi-org.brum.beds.ac.uk/10.3390/s22207814 - 14 Oct 2022

Cited by 18 | Viewed by 2765

Abstract

Geohazard prevention and mitigation are highly complex and remain challenges for researchers and practitioners. Artificial intelligence (AI) has become an effective tool for addressing these challenges. Therefore, for decades, an increasing number of researchers have begun to conduct AI research in the field of geohazards leading to rapid growth in the number of related papers. This has made it difficult for researchers and practitioners to grasp information on cutting-edge developments in the field, thus necessitating a comprehensive review and analysis of the current state of development in the field. In this study, a comprehensive scientometric analysis appraising the state-of-the-art research for geohazard was performed based on 9226 scientometric records from the Web of Science core collection database. Multiple types of scientometric techniques, including coauthor analysis, co-citation analysis, and cluster analysis were employed to identify the most productive researchers, institutions, and hot research topics. The results show that research related to the application of AI in the field of geohazards experienced a period of rapid growth after 2000, with major developments in the field occurring in China, the United States, and Italy. The hot research topics in this field are ground motion, deep learning (DL), and landslides. The commonly used AI algorithms include DL, support vector machine (SVM), and decision tree (DT). The obtained visualization on research networks offers valuable insights and an in-depth understanding of the key researchers, institutions, fundamental articles, and salient topics through animated maps. We believe that this scientometric review offers useful reference points for early-stage researchers and provides valuable in-depth information to experienced researchers and practitioners in the field of geohazard research. This scientometric analysis and visualization are promising for reflecting the global picture of AI-based geohazard research comprehensively and possess potential for the visualization of the emerging trends in other research fields. Full article

(This article belongs to the Special Issue Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard)

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