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Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology II

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 25258

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Special Issue Editors

Dr. Paraskevas Tsangaratos

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Guest Editor

Department of Geological Studies, School of Mining and Metallurgical Engineering, National University of Athens, 15773 Athens, Greece
Interests: natural hazards; water resources; engineering geology; GIS; machine learning; soft computing; remote sensing
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Dr. Wei Chen

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Guest Editor

College of Geology & Environment, Xi’an University of Science and Technology, Xi’an 710054, China
Interests: hydrogeology; environmental impact assessment; natural hazard susceptibility; spatial modeling; machine learning; geology; civil engineering
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Dr. Ioanna Ilia

E-Mail Website
Guest Editor

Department of Geological Studies, School of Mining and Metallurgical Engineering, National University of Athens, 15773 Athens, Greece
Interests: natural hazards; environmental geology; engineering geology; GIS; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Haoyuan Hong

E-Mail Website
Guest Editor

School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: natural hazards; water resources; machine learning; soft computing; GIS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is well-known that natural hazards are often responsible for severe financial and human losses across the world. Natural hazards, which involve earthquakes, floods, landslides, volcanic eruptions, wildfires, droughts, soil erosion and degradation, are the result of progressive or extreme changes in climatic, tectonic and geo-morphological processes but also the impact of human activities on the geo-environment. Their complex nature, variation in frequency, speed, duration and area affected are some of the characteristics that obscure our full understanding of the mechanism behind their evolution and extent of occurrence. The main focus of scientists from various geophysical disciplines is on creating conceptual models, developing intelligent computing techniques and machine learning (ML) algorithms, and applying remote sensing (RS) technology within a geographic information system (GIS) framework that captures their complex nature and provides accurate prediction concerning their spatial and temporal occurrence. ML algorithms provide a “recipe” to computers of how to learn from existing data, produce knowledge and discover hidden and unknown patterns and trends from large databases, whereas GIS is a significant technology equipped with tools for data manipulation and advanced modeling. In recent years, ML, which includes algorithms and methods that are based on the concept of fuzzy and neuro-fuzzy logic, decision tree models, artificial neural networks, deep learning (convolutional neural network, recurrent neural networks, auto-encoders), ensemble methods (bagging, boosting, stacking) and evolutionary algorithms (ant colony optimization, particle swarm optimization, genetic algorithms, etc.), along with GIS and RS technology, have been proposed as alternative investigation tools for natural risk phenomena, susceptibility and hazardous mapping.

This Special Issue aims to provide an outlet for peer-reviewed publications that implement state-of-the-art methods and techniques incorporating RS technology, ML methods and GIS so as to map, monitor, evaluate, and assess natural hazards.

Potential topics of interest (but not limited to) include regional or global case studies concerning natural risk phenomena prediction and assessment, software development and implementation of machine learning, optimization, deep learning techniques, and meta-heuristic algorithms. Specifically, this Special Issue aims to cover, without being limited to, the following areas:

Monitoring, mapping and assessing earthquakes, landslides, floods, wildfires, soil erosion, and land subsidence.
Evaluating loss and damage after earthquakes, floods, landslides, wildfires, soil erosion, and land subsidence.

Dr. Paraskevas Tsangaratos
Dr. Wei Chen
Dr. Ioanna Ilia
Dr. Haoyuan Hong
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

earth observation data – remote sensing technology
geographic information systems
machine learning, soft computing
landslide susceptibility, hazardous and risk mapping
flood susceptibility mapping and disaster management
wildfire susceptibility mapping
soil erosion/degradation
earthquakes/tsunamis

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Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology: 3rd Edition in Remote Sensing (1 article)

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22 pages, 46483 KiB

Open AccessArticle

SWIFT: Simulated Wildfire Images for Fast Training Dataset

by Luiz Fernando, Rafik Ghali and Moulay A. Akhloufi

Remote Sens. 2024, 16(9), 1627; https://doi.org/10.3390/rs16091627 - 2 May 2024

Cited by 1 | Viewed by 1633

Abstract

Wildland fires cause economic and ecological damage with devastating consequences, including loss of life. To reduce these risks, numerous fire detection and recognition systems using deep learning techniques have been developed. However, the limited availability of annotated datasets has decelerated the development of reliable deep learning techniques for detecting and monitoring fires. For such, a novel dataset, namely, SWIFT, is presented in this paper for detecting and recognizing wildland smoke and fires. SWIFT includes a large number of synthetic images and videos of smoke and wildfire with their corresponding annotations, as well as environmental data, including temperature, humidity, wind direction, and speed. It represents various wildland fire scenarios collected from multiple viewpoints, covering forest interior views, views near active fires, ground views, and aerial views. In addition, three deep learning models, namely, BoucaNet, DC-Fire, and CT-Fire, are adopted to recognize forest fires and address their related challenges. These models are trained using the SWIFT dataset and tested using real fire images. BoucaNet performed well in recognizing wildland fires and overcoming challenging limitations, including the complexity of the background, the variation in smoke and wildfire features, and the detection of small wildland fire areas. This shows the potential of sim-to-real deep learning in wildland fires. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology II)

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20 pages, 4551 KiB

Open AccessArticle

Impacts of Resampling and Downscaling Digital Elevation Model and Its Morphometric Factors: A Comparison of Hopfield Neural Network, Bilinear, Bicubic, and Kriging Interpolations

by Nguyen Quang Minh, Nguyen Thi Thu Huong, Pham Quoc Khanh, La Phu Hien and Dieu Tien Bui

Remote Sens. 2024, 16(5), 819; https://doi.org/10.3390/rs16050819 - 27 Feb 2024

Cited by 4 | Viewed by 2683

Abstract

The digital elevation model (DEM) and its derived morphometric factors, i.e., slope, aspect, profile and plan curvatures, and topographic wetness index (TWI), are essential for natural hazard modeling and prediction as they provide critical information about the terrain’s characteristics that can influence the likelihood and severity of natural hazards. Therefore, increasing the accuracy of the DEM and its derived factors plays a critical role. The primary aim of this study is to evaluate and compare the effects of resampling and downscaling the DEM from low to medium resolution and from medium to high resolutions using four methods: namely the Hopfield Neural Network (HNN), Bilinear, Bicubic, and Kriging, on five morphometric factors derived from it. A geospatial database was established, comprising five DEMs with different resolutions: specifically, a SRTM DEM with 30 m resolution, a 20 m resolution DEM derived from topographic maps at a scale of 50,000, a 10 m resolution DEM generated from topographic maps at a scale of 10,000, a 5 m resolution DEM created using surveying points with total stations, and a 5 m resolution DEM constructed through drone photogrammetry. The accuracy of the resampling and downscaling was assessed using Root Mean Square Error (RMSE) and mean absolute error (MAE) as statistical metrics. The results indicate that, in the case of downscaling from low to medium resolution, all four methods—HNN, Bilinear, Bicubic, and Kriging—significantly improve the accuracy of slope, aspect, profile and plan curvatures, and TWI. However, for the case of medium to high resolutions, further investigations are needed as the improvement in accuracy observed in the DEMs does not necessarily translate to the improvement of the second derivative morphometric factors such as plan and profile curvatures and TWI. While RMSEs of the first derivatives of DEMs, such as slope and aspect, reduced in a range of 8% to 55% in all five datasets, the RMSEs of curvatures and TWI slightly increased in cases of downscaling and resampling of Dataset 4. Among the four methods, the HNN method provides the highest accuracy, followed by the bicubic method. The statistics showed that in all five cases of the experiment, the HNN downscaling reduced the RMSE and MAE by 55% for the best case and 10% for the worst case for slope, and it reduced the RMSE by 50% for the best case of aspect. Both the HNN and the bicubic methods outperform the Kriging and bilinear methods. Therefore, we highly recommend using the HNN method for downscaling DEMs to produce more accurate morphometric factors, slope, aspect, profile and plan curvatures, and TWI. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology II)

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21 pages, 7502 KiB

Open AccessArticle

A Novel Approach to Enhance Landslide Displacement Prediction with Finer Monitoring Data: A Case Study of the Baijiabao Landslide

by Ding Xia, Huiming Tang and Thomas Glade

Remote Sens. 2024, 16(4), 618; https://doi.org/10.3390/rs16040618 - 7 Feb 2024

Cited by 1 | Viewed by 1171

Abstract

Rainfall and reservoir water level are commonly regarded as the two major influencing factors for reservoir landslides and are employed for landslide displacement prediction, yet their daily data are readily available with current monitoring technology, which makes a more refined analysis possible. However, until now, few efforts have been made to predict landslide displacements using daily data, which is likely to substantially improve accuracy and is crucial for landslide early warning. A novel feature enhancement approach for extracting critical characteristics from daily rainfall and reservoir water level data for use in landslide displacement prediction is proposed in this study. Six models, including gated recurrent units (GRUs), long short-term memory (LSTM), and support vector regression (SVR) with an unenhanced dataset and GRU-E, LSTM-E, and SVR-E with an enhanced dataset, were employed for displacement predictions at four GPS monitoring stations on the Baijiabao landslide, a typical step-like reservoir landslide. The results show that the accuracy values of all the enhanced models were significantly improved, and the GRU-E model achieved the most significant improvement, with the RMSE decreasing by 24.39% and R² increasing by 0.2693, followed by the LSTM-E and SVR-E models. Further, the GRU-E model consistently outperformed the other models, achieving the highest R² of 0.6265 and the lowest RMSE of 16.5208 mm, significantly superior than the others. This study indicates the feasibility of improving the accuracy of landslide monthly displacement predictions with finer monitoring data and provides valuable insights for future research. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology II)

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25 pages, 10772 KiB

Open AccessArticle

Uncertainty Reduction in Flood Susceptibility Mapping Using Random Forest and eXtreme Gradient Boosting Algorithms in Two Tropical Desert Cities, Shibam and Marib, Yemen

by Ali R. Al-Aizari, Hassan Alzahrani, Omar F. AlThuwaynee, Yousef A. Al-Masnay, Kashif Ullah, Hyuck-Jin Park, Nabil M. Al-Areeq, Mahfuzur Rahman, Bashar Y. Hazaea and Xingpeng Liu

Remote Sens. 2024, 16(2), 336; https://doi.org/10.3390/rs16020336 - 15 Jan 2024

Cited by 6 | Viewed by 2675

Abstract

Flooding is a natural disaster that coexists with human beings and causes severe loss of life and property worldwide. Although numerous studies for flood susceptibility modelling have been introduced, a notable gap has been the overlooked or reduced consideration of the uncertainty in the accuracy of the produced maps. Challenges such as limited data, uncertainty due to confidence bounds, and the overfitting problem are critical areas for improving accurate models. We focus on the uncertainty in susceptibility mapping, mainly when there is a significant variation in the predictive relevance of the predictor factors. It is also noted that the receiver operating characteristic (ROC) curve may not accurately depict the sensitivity of the resulting susceptibility map to overfitting. Therefore, reducing the overfitting problem was targeted to increase accuracy and improve processing time in flood prediction. This study created a spatial repository to test the models, containing data from historical flooding and twelve topographic and geo-environmental flood conditioning variables. Then, we applied random forest (RF) and extreme gradient boosting (XGB) algorithms to map flood susceptibility, incorporating a variable drop-off in the empirical loop function. The results showed that the drop-off loop function was a crucial method to resolve the model uncertainty associated with the conditioning factors of the susceptibility modelling and methods. The results showed that approximately 8.42% to 9.89% of Marib City and 9.93% to 15.69% of Shibam City areas were highly vulnerable to floods. Furthermore, this study significantly contributes to worldwide endeavors focused on reducing the hazards linked to natural disasters. The approaches used in this study can offer valuable insights and strategies for reducing natural disaster risks, particularly in Yemen. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology II)

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22 pages, 20986 KiB

Open AccessArticle

Risk Assessment and Analysis of Its Influencing Factors of Debris Flows in Typical Arid Mountain Environment: A Case Study of Central Tien Shan Mountains, China

by Zhi Li, Mingyang Wu, Ningsheng Chen, Runing Hou, Shufeng Tian and Mahfuzur Rahman

Remote Sens. 2023, 15(24), 5681; https://doi.org/10.3390/rs15245681 - 11 Dec 2023

Cited by 1 | Viewed by 1797

Abstract

The Tien Shan Mountain range connects Central Asia with northwestern China and is a crucial transport junction between East and West Asia. It is a common location for regional debris flows, which pose a significant risk to ecological security and the safety of people and property. Nevertheless, limited knowledge exists about the distribution of disaster risks and the impacted populations. This study uses advanced machine learning techniques to identify the key natural and social factors influencing these hazards and incorporates the Social Vulnerability Index (SoVI) to assess societal vulnerability. The outcomes demonstrate that (1) the debris flow hazard in the Tien Shan Mountain area is primarily governed by the geological structure, which dictates the material source and, in turn, dictates the onset of debris flows. (2) The vulnerability demonstrates a high spatial tendency in the north and a low one in the south, with evident spatial clustering characteristics. (3) A total of 19.13% of the study area is classified as high-hazard, with specific distribution zones including the northern foothills of the Tien Shan Mountains, the low-mountain zones of the southern foothills of the Tien Shan Mountains, and the Yili Valley zone. This holistic approach offers valuable insights into the spatial distribution of risks, aiding in prioritizing disaster preparedness and mitigation efforts. Also, our findings and conclusions are beneficial for local decision makers to allocate resources effectively and promote sustainable development practices in the region. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology II)

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24 pages, 22247 KiB

Open AccessArticle

Spatial Prediction of Fluvial Flood in High-Frequency Tropical Cyclone Area Using TensorFlow 1D-Convolution Neural Networks and Geospatial Data

by Nguyen Gia Trong, Pham Ngoc Quang, Nguyen Van Cuong, Hong Anh Le, Hoang Long Nguyen and Dieu Tien Bui

Remote Sens. 2023, 15(22), 5429; https://doi.org/10.3390/rs15225429 - 20 Nov 2023

Cited by 5 | Viewed by 1785

Abstract

Fluvial floods endure as one of the most catastrophic weather-induced disasters worldwide, leading to numerous fatalities each year and significantly impacting socio-economic development and the environment. Hence, the research and development of new methods and algorithms focused on improving fluvial flood prediction and devising robust flood management strategies are essential. This study explores and assesses the potential application of 1D-Convolution Neural Networks (1D-CNN) for spatial prediction of fluvial flood in the Quang Nam province, a high-frequency tropical cyclone area in central Vietnam. To this end, a geospatial database with 4156 fluvial flood locations and 12 flood indicators was considered. The ADAM algorithm and the MSE loss function were used to train the 1D-CNN model, whereas popular performance metrics, such as Accuracy (Acc), Kappa, and AUC, were used to measure the performance. The results indicated remarkable performance by the 1D-CNN model, achieving high prediction accuracy with metrics such as Acc = 90.7%, Kappa = 0.814, and AUC = 0.963. Notably, the proposed 1D-CNN model outperformed benchmark models, including DeepNN, SVM, and LR. This achievement underscores the promise and innovation brought by 1D-CNN in the realm of susceptibility mapping for fluvial floods. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology II)

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34 pages, 35632 KiB

Open AccessArticle

Spatial Prediction of Landslide Susceptibility Using Logistic Regression (LR), Functional Trees (FTs), and Random Subspace Functional Trees (RSFTs) for Pengyang County, China

by Hui Shang, Lixiang Su, Wei Chen, Paraskevas Tsangaratos, Ioanna Ilia, Sihang Liu, Shaobo Cui and Zhao Duan

Remote Sens. 2023, 15(20), 4952; https://doi.org/10.3390/rs15204952 - 13 Oct 2023

Cited by 10 | Viewed by 2073

Abstract

Landslides pose significant and serious geological threat disasters worldwide, threatening human lives and property; China is particularly susceptible to these disasters. This paper focuses on Pengyang County, which is situated in the Ningxia Hui Autonomous Region of China, an area prone to landslides. This study investigated the application of machine learning techniques for analyzing landslide susceptibility. To construct and validate the model, we initially compiled a landslide inventory comprising 972 historical landslides and an equivalent number of non-landslide sites (Data sourced from the Pengyang County Department of Natural Resources). To ensure an impartial evaluation, both the landslide and non-landslide datasets were randomly divided into two sets using a 70/30 ratio. Next, we extracted 15 landslide conditioning factors, including the slope angle, elevation, profile curvature, plan curvature, slope aspect, TWI (topographic wetness index), TPI (topographic position index), distance to roads and rivers, NDVI (normalized difference vegetation index), rainfall, land use, lithology, SPI (stream power index), and STI (sediment transport index), from the spatial database. Subsequently, a correlation analysis between the conditioning factors and landslide occurrences was conducted using the certainty factor (CF) method. Three landslide models were established by employing logistic regression (LR), functional trees (FTs), and random subspace functional trees (RSFTs) algorithms. The landslide susceptibility map was categorized into five levels: very low, low, medium, high, and very high susceptibility. Finally, the predictive capability of the three algorithms was assessed using the area under the receiver operating characteristic curve (AUC). The better the prediction, the higher the AUC value. The results indicate that all three models are predictive and practical, with only minor discrepancies in accuracy. The integrated model (RSFT) displayed the highest predictive performance, achieving an AUC value of 0.844 for the training dataset and 0.837 for the validation dataset. This was followed by the LR model (0.811 for the training dataset and 0.814 for the validation dataset) and the FT model (0.776 for the training dataset and 0.760 for the validation dataset). The proposed methods and resulting landslide susceptibility map can assist researchers and local authorities in making informed decisions for future geohazard prevention and mitigation. Furthermore, they will prove valuable and be useful for other regions with similar geological characteristics features. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology II)

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23 pages, 11462 KiB

Open AccessArticle

Residual Attention Mechanism for Remote Sensing Target Hiding

by Hao Yuan, Yongjian Shen, Ning Lv, Yuheng Li, Chen Chen and Zhouzhou Zhang

Remote Sens. 2023, 15(19), 4731; https://doi.org/10.3390/rs15194731 - 27 Sep 2023

Viewed by 1222

Abstract

In this paper, we investigate deep-learning-based image inpainting techniques for emergency remote sensing mapping. Image inpainting can generate fabricated targets to conceal real-world private structures and ensure informational privacy. However, casual inpainting outputs may seem incongruous within original contexts. In addition, the residuals of original targets may persist in the hiding results. A Residual Attention Target-Hiding (RATH) model has been proposed to address these limitations for remote sensing target hiding. The RATH model introduces the residual attention mechanism to replace gated convolutions, thereby reducing parameters, mitigating gradient issues, and learning the distribution of targets present in the original images. Furthermore, this paper modifies the fusion module in the contextual attention layer to enlarge the fusion patch size. We extend the edge-guided function to preserve the original target information and confound viewers. Ablation studies on an open dataset proved the efficiency of RATH for image inpainting and target hiding. RATH had the highest similarity, with a

90.44 %

structural similarity index metric (SSIM), for edge-guided target hiding. The training parameters had

1 M

fewer values than gated convolution (Gated Conv). Finally, we present two automated target-hiding techniques that integrate semantic segmentation with direct target hiding or edge-guided synthesis for remote sensing mapping applications. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology II)

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18 pages, 5682 KiB

Open AccessArticle

Automatic Detection of Forested Landslides: A Case Study in Jiuzhaigou County, China

by Dongfen Li, Xiaochuan Tang, Zihan Tu, Chengyong Fang and Yuanzhen Ju

Remote Sens. 2023, 15(15), 3850; https://doi.org/10.3390/rs15153850 - 2 Aug 2023

Cited by 16 | Viewed by 2645

Abstract

Landslide detection and distribution mapping are essential components of geohazard prevention. For the extremely difficult problem of automatic forested landslide detection, airborne remote sensing technologies, such as LiDAR and optical cameras, can obtain more accurate landslide monitoring data. In practice, however, airborne LiDAR data and optical images are treated independently. The complementary information of the remote sensing data from multiple sources has not been thoroughly investigated. To address this deficiency, we investigate how to use LiDAR data and optical images together to develop an automatic detection model for forested landslide detection. First, a new dataset for detecting forested landslides in the Jiuzhaigou earthquake region is compiled. LiDAR-derived DEM and hillshade maps are used to mitigate the influence of forest cover on the detection of forested landslides. Second, a new deep learning model called DemDet is proposed for the automatic detection of forested landslides. In the feature extraction component of DemDet, a self-supervised learning module is proposed for extracting geometric features from LiDAR-derived DEM. Additionally, a transformer-based deep neural network is proposed for identifying landslides from hillshade maps and optical images. In the data fusion component of DemDet, an attention-based neural network is proposed to combine DEM, hillshade, and optical images. DemDet is able to extract key features from hillshade images, optical images, and DEM, as demonstrated by experimental results on the proposed dataset. In comparison to ResUNet, LandsNet, HRNet, MLP, and SegFormer, DemDet obtains the highest mean accuracy, mIoU, and F1 values, namely 0.95, 0.67, and 0.777. DemDet is therefore capable of autonomously identifying the forest-covered landslides in the Jiuzhaigou earthquake zone. The results of landslide detection mapping reveal that slopes along roads and seismogenic faults are the most crucial areas requiring geohazard prevention. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology II)

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30 pages, 20252 KiB

Open AccessArticle

Applying a 1D Convolutional Neural Network in Flood Susceptibility Assessments—The Case of the Island of Euboea, Greece

by Paraskevas Tsangaratos, Ioanna Ilia, Aikaterini-Alexandra Chrysafi, Ioannis Matiatos, Wei Chen and Haoyuan Hong

Remote Sens. 2023, 15(14), 3471; https://doi.org/10.3390/rs15143471 - 10 Jul 2023

Cited by 12 | Viewed by 1861

Abstract

The main scope of the study is to evaluate the prognostic accuracy of a one-dimensional convolutional neural network model (1D-CNN), in flood susceptibility assessment, in a selected test site on the island of Euboea, Greece. Logistic regression (LR), Naïve Bayes (NB), gradient boosting (GB), and a deep learning neural network (DLNN) model are the benchmark models used to compare their performance with that of a 1D-CNN model. Remote sensing (RS) techniques are used to collect the necessary flood related data, whereas thirteen flash-flood-related variables were used as predictive variables, such as elevation, slope, plan curvature, profile curvature, topographic wetness index, lithology, silt content, sand content, clay content, distance to faults, and distance to river network. The Weight of Evidence method was applied to calculate the correlation among the flood-related variables and to assign a weight value to each variable class. Regression analysis and multi-collinearity analysis were used to assess collinearity among the flood-related variables, whereas the Shapley Additive explanations method was used to rank the features by importance. The evaluation process involved estimating the predictive ability of all models via classification accuracy, sensitivity, specificity, and area under the success and predictive rate curves (AUC). The outcomes of the analysis confirmed that the 1D-CNN provided a higher accuracy (0.924), followed by LR (0.904) and DLNN (0.899). Overall, 1D-CNNs can be useful tools for analyzing flood susceptibility using remote sensing data, with high accuracy predictions. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology II)

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21 pages, 98313 KiB

Open AccessArticle

Hybrid BBO-DE Optimized SPAARCTree Ensemble for Landslide Susceptibility Mapping

by Duc Anh Hoang, Hung Van Le, Dong Van Pham, Pham Viet Hoa and Dieu Tien Bui

Remote Sens. 2023, 15(8), 2187; https://doi.org/10.3390/rs15082187 - 20 Apr 2023

Cited by 2 | Viewed by 1894

Abstract

This paper presents a new hybrid ensemble modeling method called BBO-DE-STreeEns for land-slide susceptibility mapping in Than Uyen district, Vietnam. The method uses subbagging and random subspacing to generate subdatasets for constituent classifiers of the ensemble model, and a split-point and attribute reduced classifier (SPAARC) decision tree algorithm to build each classifier. To optimize hyperparameters of the ensemble model, a hybridization of biogeography-based optimization (BBO) and differential evolution (DE) algorithms is adopted. The land-slide database for the study area includes 114 landslide locations, 114 non-landslide locations, and ten influencing factors: elevation, slope, curvature, aspect, relief amplitude, soil type, geology, distance to faults, distance to roads, and distance to rivers. The database was used to build and verify the BBO-DE-StreeEns model, and standard statistical metrics, namely, positive predictive value (PPV), negative predictive value (NPV), sensitivity (Sen), specificity (Spe), accuracy (Acc), Fscore, Cohen’s Kappa, and the area under the ROC curve (AUC), were calculated to evaluate prediction power. Logistic regression, multi-layer perceptron neural network, support vector machine, and SPAARC were used as benchmark models. The results show that the proposed model outperforms the benchmarks with a high prediction power (PPV = 90.3%, NPV = 83.8%, Sen = 82.4%, Spe = 91.2%, Acc = 86.8%, Fscore = 0.862, Kappa = 0.735, and AUC = 0.940). Therefore, the BBO-DE-StreeEns method is a promising tool for landslide susceptibility mapping. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology II)

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21 pages, 24697 KiB

Open AccessArticle

GIS-Based Landslide Susceptibility Modeling: A Comparison between Best-First Decision Tree and Its Two Ensembles (BagBFT and RFBFT)

by Jingyun Gui, Leandro Rafael Alejano, Miao Yao, Fasuo Zhao and Wei Chen

Remote Sens. 2023, 15(4), 1007; https://doi.org/10.3390/rs15041007 - 11 Feb 2023

Cited by 6 | Viewed by 2328

Abstract

This study aimed to explore and compare the application of current state-of-the-art machine learning techniques, including bagging (Bag) and rotation forest (RF), to assess landslide susceptibility with the base classifier best-first decision tree (BFT). The proposed two novel ensemble frameworks, BagBFT and RFBFT, and the base model BFT, were used to model landslide susceptibility in Zhashui County (China), which suffers from landslides. Firstly, we identified 169 landslides through field surveys and image interpretation. Then, a landslide inventory map was built. These 169 historical landslides were randomly classified into two groups: 70% for training data and 30% for validation data. Then, 15 landslide conditioning factors were considered for mapping landslide susceptibility. The three ensemble outputs were estimated with a receiver operating characteristic (ROC) curve and statistical tests, as well as a new approach, the improved frequency ratio accuracy. The areas under the ROC curve (AUCs) for the training data (success rate) of the three algorithms were 0.722 for BFT, 0.869 for BagBFT, and 0.895 for RFBFT. The AUCs for the validating groups (prediction rates) were 0.718, 0.834, and 0.872, respectively. The frequency ratio accuracy of the three models was 0.76163 for the BFT model, 0.92220 for the BagBFT model, and 0.92224 for the RFBFT model. Both BagBFT and RFBFT ensembles can improve the accuracy of the BFT base model, and RFBFT was relatively better. Therefore, the RFBFT model is the most effective approach for the accurate modeling of landslide susceptibility mapping (LSM). All three models can improve the identification of landslide-prone areas, enhance risk management ability, and afford more detailed information for land-use planning and policy setting. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology II)

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