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Spatial Modelling of Natural Hazards and Water Resources through Remote Sensing, GIS and Machine Learning Methods

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 59178

Special Issue Editors

Dr. Paraskevas Tsangaratos

E-Mail Website
Guest Editor
Department of Geological Studies, School of Mining and Metallurgical Engineering, National University of Athens, 15780 Athens, Greece
Interests: natural hazards; water resources; engineering geology; GIS; machine learning; soft computing; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Wei Chen

E-Mail Website
Guest Editor
College of Geology & Environment, Xi’an University of Science and Technology, Xi’an, China
Interests: hydrogeology; environmental impact assessment; natural hazard susceptibility; spatial modeling; machine learning; geology; civil engineering
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 and 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,

The main objective of this Special Issue on “Spatial Modeling of Natural Hazards and Water Resources through Remote Sensing, GIS, and Machine Learning Methods” is to provide a scientific forum for advancing the successful implementation of remote sensing technologies (RS), geographic information systems (GIS), and machine learning (ML) methods in natural hazard and water resource assessments.

RS technology is considered among the scientific community as an important investigation tool ideal for problems and arising issues concerning urban development, environmental monitoring, natural hazard damage and risk assessment, water resource management, and land use/cover change monitoring and modeling. Along with GIS, RS appears as the most significant and advanced technology for spatial and temporal data manipulation and advanced modeling. ML methods, which involve, among others, methods based on the concept of fuzzy and neurofuzzy logic, decision tree, artificial neural network, deep learning and evolutionary algorithms, have been reported as highly sophisticated methods for classification and regression problems. At the moment, they appear as cutting-edge methods and techniques for discovering hidden and unknown patterns and trends from large databases.

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 and water resources. This Special Issue aims to cover, without being limited to, the following areas:

  • Hydrologic and hydrogeological modeling of surface water;
  • Groundwater monitoring;
  • Groundwater spring potential mapping;
  • Evaluating loss and damages after earthquakes, floods, landslides and wildfires;
  • Monitoring, mapping, and assessing earthquakes, landslides, floods, and wildfires.

Dr. Paraskevas Tsangaratos
Dr. Wei Chen
Mr. Haoyuan Hong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Earth observation data
  • Geographic information systems
  • Machine learning
  • Soft computing
  • Susceptibility, hazardous, and risk mapping
  • Hydrologic/hydrogeological modelling
  • Groundwater monitoring

Published Papers (16 papers)

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Research

21 pages, 11456 KiB  
Article
Coupling Data- and Knowledge-Driven Methods for Landslide Susceptibility Mapping in Human-Modified Environments: A Case Study from Wanzhou County, Three Gorges Reservoir Area, China
by Lanbing Yu, Chao Zhou, Yang Wang, Ying Cao and David J. Peres
Remote Sens. 2022, 14(3), 774; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030774 - 07 Feb 2022
Cited by 12 | Viewed by 2059
Abstract
Landslide susceptibility mapping (LSM) can provide valuable information for local governments in landslide prevention and mitigation. Despite significant improvements in the predictive performance of LSM, it remains a challenge to be carried out in areas with limited availability of data. For example, in [...] Read more.
Landslide susceptibility mapping (LSM) can provide valuable information for local governments in landslide prevention and mitigation. Despite significant improvements in the predictive performance of LSM, it remains a challenge to be carried out in areas with limited availability of data. For example, in the early stage of road construction, landslide inventory data can be particularly scarce, while there is a high need to have a susceptibility map. This study aims to set up a novel procedure for coupling the knowledge-driven and data-driven models for LSM in an area with limited landslide inventory data. In particular, we propose a two-step approach. The first step consists of applying four data-driven models (logistic regression, decision tree, support vector machines, and random forest (RF)) to derive a regional susceptibility map. In the second step, the application of a heuristic model (analytic hierarchy process, AHP) is proposed to calculate a local susceptibility map for the areas with incomplete landslide inventories. The final landslide susceptibility map is obtained by merging the most accurate regional map (RF) with the local map. We apply this novel procedure to a landslide-prone region with developed road construction (National Highway G69) in Wanzhou district, where landslide inventory is difficult to update due to timely recovery from landslide-induced road damage. Results show that the proposed methodology allows identifying new landslide-prone areas, and improving LSM predictive performance, as demonstrated by the fact that two new landslides developed along G69 were perfectly classified in the highly susceptible areas. The results show that implementing the landslide susceptibility assessment with different geographical settings and combining them into best-sensitivity partitions is more accurate than focusing on creating new models or hybrid models. Full article
(This article belongs to the Special Issue Spatial Modelling of Natural Hazards and Water Resources through Remote Sensing, GIS and Machine Learning Methods)
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23 pages, 14360 KiB  
Article
A Simple Method of Mapping Landslides Runout Zones Considering Kinematic Uncertainties
by Jia Liu, Yuming Wu, Xing Gao and Xuehua Zhang
Remote Sens. 2022, 14(3), 668; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030668 - 30 Jan 2022
Cited by 11 | Viewed by 3066
Abstract
Landslides can be triggered by natural and human activities, threatening the safety of buildings and infrastructures. Mapping potential landslide runout zones are critical for regional risk evaluation. Although remote sensing technology has been widely used to discover unstable areas, an entire landslide runout [...] Read more.
Landslides can be triggered by natural and human activities, threatening the safety of buildings and infrastructures. Mapping potential landslide runout zones are critical for regional risk evaluation. Although remote sensing technology has been widely used to discover unstable areas, an entire landslide runout zone is difficult to identify using these techniques alone. Some simplified methods based on empirical models are used to simulate full-scale movements, but these methods do not consider the kinematic uncertainties caused by random particle collisions in practice. In this paper, we develop a semi-empirical landslide dynamics method considering kinematic uncertainties to solve this problem. The uncertainties caused by the microtopography and anisotropy of the material are expressed by the diffusion angle. Monte Carlo (MC) simulations are adopted to calculate the probability of each cell. Compared with the existing Flow-R model, this method can more accurately and effectively estimate runout zones of the Yigong landslide where random particle collisions are intense. Combining the D-InSAR technique, we evaluate the runout zones in the Jinsha River from June 2019 to December 2020. This result shows that the method is of great significance in early warning and risk mitigation, especially in remote areas. The source area of the landslide and DEM resolution together affect the number of MC simulations required. A landslide with a larger volume requires a larger diffusion angle and more MC simulations. Full article
(This article belongs to the Special Issue Spatial Modelling of Natural Hazards and Water Resources through Remote Sensing, GIS and Machine Learning Methods)
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15 pages, 3801 KiB  
Article
Transfer Learning for Improving Seismic Building Damage Assessment
by Qigen Lin, Tianyu Ci, Leibin Wang, Sanjit Kumar Mondal, Huaxiang Yin and Ying Wang
Remote Sens. 2022, 14(1), 201; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010201 - 02 Jan 2022
Cited by 19 | Viewed by 2696
Abstract
The rapid assessment of building damage in earthquake-stricken areas is of paramount importance for emergency response. The development of remote sensing technology has aided in deriving reliable and precise building damage assessments of extensive areas following disasters. It is well documented that convolutional [...] Read more.
The rapid assessment of building damage in earthquake-stricken areas is of paramount importance for emergency response. The development of remote sensing technology has aided in deriving reliable and precise building damage assessments of extensive areas following disasters. It is well documented that convolutional neural network methods have superior performance in earthquake building damage assessment compared with traditional machine learning methods. However, deep learning models require a large number of samples, and sufficient numbers of samples are usually not available in the newly earthquake-stricken areas rapidly enough. At the same time, the historical samples inevitably differ from the new earthquake-affected areas due to the discrepancy of regional building characteristics. For this purpose, this study proposes a data transfer algorithm for evaluating the impact of a single historical training sample on the model performance. Then, beneficial samples are selected to transfer knowledge from the historical data for facilitating the calibration of the new model. Four models are designed with two earthquake damage building datasets and the performance of the models is compared and evaluated. The results show that the data transfer algorithm proposed in this work improves the reliability of the building damage assessment model significantly by filtering samples from the historical data that are suitable for the new task. The performance of the model built based on the data transfer method on the test set of new earthquakes task is approximately 8% higher in overall accuracy compared with the model trained directly with the new earthquake samples when the training data for the new task is only 10% of the historical data and is operating under the objective of four classes of building damage. The proposed data transfer algorithm has effectively enhanced the precision of the seismic building damage assessment in a data-limited context. Thus, it could be applicable to the building damage assessment of new disasters. Full article
(This article belongs to the Special Issue Spatial Modelling of Natural Hazards and Water Resources through Remote Sensing, GIS and Machine Learning Methods)
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28 pages, 8787 KiB  
Article
Hybrids of Support Vector Regression with Grey Wolf Optimizer and Firefly Algorithm for Spatial Prediction of Landslide Susceptibility
by Ru Liu, Jianbing Peng, Yanqiu Leng, Saro Lee, Mahdi Panahi, Wei Chen and Xia Zhao
Remote Sens. 2021, 13(24), 4966; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13244966 - 07 Dec 2021
Cited by 16 | Viewed by 2660
Abstract
Landslides are one of the most frequent and important natural disasters in the world. The purpose of this study is to evaluate the landslide susceptibility in Zhenping County using a hybrid of support vector regression (SVR) with grey wolf optimizer (GWO) and firefly [...] Read more.
Landslides are one of the most frequent and important natural disasters in the world. The purpose of this study is to evaluate the landslide susceptibility in Zhenping County using a hybrid of support vector regression (SVR) with grey wolf optimizer (GWO) and firefly algorithm (FA) by frequency ratio (FR) preprocessed. Therefore, a landslide inventory composed of 140 landslides and 16 landslide conditioning factors is compiled as a landslide database. Among these landslides, 70% (98) landslides were randomly selected as the training dataset of the model, and the other landslides (42) were used to verify the model. The 16 landslide conditioning factors include elevation, slope, aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, sediment transport index (STI), stream power index (SPI), topographic wetness index (TWI), normalized difference vegetation index (NDVI), landslide, rainfall, soil and lithology. The conditioning factors selection and spatial correlation analysis were carried out by using the correlation attribute evaluation (CAE) method and the frequency ratio (FR) algorithm. The area under the receiver operating characteristic curve (AUROC) and kappa data of the training dataset and validation dataset are used to evaluate the prediction ability and the relationship between the advantages and disadvantages of landslide susceptibility maps. The results show that the SVR-GWO model (AUROC = 0.854) has the best performance in landslide spatial prediction, followed by the SVR-FA (AUROC = 0.838) and SVR models (AUROC = 0.818). The hybrid models of SVR-GWO and SVR-FA improve the performance of the single SVR model, and all three models have good prospects for regional-scale landslide spatial modeling. Full article
(This article belongs to the Special Issue Spatial Modelling of Natural Hazards and Water Resources through Remote Sensing, GIS and Machine Learning Methods)
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17 pages, 5916 KiB  
Article
Modeling the Spatial Distribution of Debris Flows and Analysis of the Controlling Factors: A Machine Learning Approach
by Yan Zhao, Xingmin Meng, Tianjun Qi, Guan Chen, Yajun Li, Dongxia Yue and Feng Qing
Remote Sens. 2021, 13(23), 4813; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234813 - 27 Nov 2021
Cited by 14 | Viewed by 2820
Abstract
Debris flows are a major geological hazard in mountainous regions. For improving mitigation, it is important to study the spatial distribution and factors controlling debris flows. In the Bailong River Basin, central China, landslides and debris flows are very well developed due to [...] Read more.
Debris flows are a major geological hazard in mountainous regions. For improving mitigation, it is important to study the spatial distribution and factors controlling debris flows. In the Bailong River Basin, central China, landslides and debris flows are very well developed due to the large differences in terrain, the complex geological environment, and concentrated rainfall. For analysis, 52 influencing factors, statistical, machine learning, remote sensing and GIS methods were used to analyze the spatial distribution and controlling factors of 652 debris flow catchments with different frequencies. The spatial distribution of these catchments was divided into three zones according to their differences in debris flow frequencies. A comprehensive analysis of the relationship between various factors and debris flows was made. Through parameter optimization and feature selection, the Extra Trees classifier performed the best, with an accuracy of 95.6%. The results show that lithology was the most important factor controlling debris flows in the study area (with a contribution of 26%), followed by landslide density and factors affecting slope stability (road density, fault density and peak ground acceleration, with a total contribution of 30%). The average annual frequency of daily rainfall > 20 mm was the most important triggering factor (with a contribution of 7%). Forest area and vegetation cover were also important controlling factors (with a total contribution of 9%), and they should be regarded as an important component of debris flow mitigation measures. The results are helpful to improve the understanding of factors influencing debris flows and provide a reference for the formulation of mitigation measures. Full article
(This article belongs to the Special Issue Spatial Modelling of Natural Hazards and Water Resources through Remote Sensing, GIS and Machine Learning Methods)
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20 pages, 96320 KiB  
Article
Automatic Features Detection in a Fluvial Environment through Machine Learning Techniques Based on UAVs Multispectral Data
by Emanuele Pontoglio, Paolo Dabove, Nives Grasso and Andrea Maria Lingua
Remote Sens. 2021, 13(19), 3983; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193983 - 05 Oct 2021
Cited by 4 | Viewed by 1900
Abstract
The present work aims to demonstrate how machine learning (ML) techniques can be used for automatic feature detection and extraction in fluvial environments. The use of photogrammetry and machine learning algorithms has improved the understanding of both environmental and anthropic issues. The developed [...] Read more.
The present work aims to demonstrate how machine learning (ML) techniques can be used for automatic feature detection and extraction in fluvial environments. The use of photogrammetry and machine learning algorithms has improved the understanding of both environmental and anthropic issues. The developed methodology was applied considering the acquisition of multiple photogrammetric images thanks to unmanned aerial vehicles (UAV) carrying multispectral cameras. These surveys were carried out in the Salbertrand area, along the Dora Riparia River, situated in Piedmont (Italy). The authors developed an algorithm able to identify and detect the water table contour concerning the landed areas: the automatic classification in ML found a valid identification of different patterns (water, gravel bars, vegetation, and ground classes) in specific hydraulic and geomatics conditions. Indeed, the RE+NIR data gave us a sharp rise in terms of accuracy by about 11% and 13.5% of F1-score average values in the testing point clouds compared to RGB data. The obtained results about the automatic classification led us to define a new procedure with precise validity conditions. Full article
(This article belongs to the Special Issue Spatial Modelling of Natural Hazards and Water Resources through Remote Sensing, GIS and Machine Learning Methods)
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24 pages, 10218 KiB  
Article
Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping
by Xin Yang, Rui Liu, Mei Yang, Jingjue Chen, Tianqiang Liu, Yuantao Yang, Wei Chen and Yuting Wang
Remote Sens. 2021, 13(11), 2166; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112166 - 01 Jun 2021
Cited by 28 | Viewed by 4808
Abstract
This study proposed a new hybrid model based on the convolutional neural network (CNN) for making effective use of historical datasets and producing a reliable landslide susceptibility map. The proposed model consists of two parts; one is the extraction of landslide spatial information [...] Read more.
This study proposed a new hybrid model based on the convolutional neural network (CNN) for making effective use of historical datasets and producing a reliable landslide susceptibility map. The proposed model consists of two parts; one is the extraction of landslide spatial information using two-dimensional CNN and pixel windows, and the other is to capture the correlated features among the conditioning factors using one-dimensional convolutional operations. To evaluate the validity of the proposed model, two pure CNN models and the previously used methods of random forest and a support vector machine were selected as the benchmark models. A total of 621 earthquake-triggered landslides in Ludian County, China and 14 conditioning factors derived from the topography, geological, hydrological, geophysical, land use and land cover data were used to generate a geospatial dataset. The conditioning factors were then selected and analyzed by a multicollinearity analysis and the frequency ratio method. Finally, the trained model calculated the landslide probability of each pixel in the study area and produced the resultant susceptibility map. The results indicated that the hybrid model benefitted from the features extraction capability of the CNN and achieved high-performance results in terms of the area under the receiver operating characteristic curve (AUC) and statistical indices. Moreover, the proposed model had 6.2% and 3.7% more improvement than the two pure CNN models in terms of the AUC, respectively. Therefore, the proposed model is capable of accurately mapping landslide susceptibility and providing a promising method for hazard mitigation and land use planning. Additionally, it is recommended to be applied to other areas of the world. Full article
(This article belongs to the Special Issue Spatial Modelling of Natural Hazards and Water Resources through Remote Sensing, GIS and Machine Learning Methods)
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26 pages, 16537 KiB  
Article
Terrain Proxy-Based Site Classification for Seismic Zonation in North Korea within a Geospatial Data-Driven Workflow
by Han-Saem Kim, Chang-Guk Sun, Moon-Gyo Lee and Hyung-Ik Cho
Remote Sens. 2021, 13(9), 1844; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091844 - 09 May 2021
Cited by 3 | Viewed by 2644
Abstract
Numerous seismic activities occur in North Korea. However, it is difficult to perform seismic hazard assessment and obtain zonal data in the Korean Peninsula, including North Korea, when applying parametric or nonparametric methods. Remote sensing can be implemented for soil characterization or spatial [...] Read more.
Numerous seismic activities occur in North Korea. However, it is difficult to perform seismic hazard assessment and obtain zonal data in the Korean Peninsula, including North Korea, when applying parametric or nonparametric methods. Remote sensing can be implemented for soil characterization or spatial zonation studies on irregular, surficial, and subsurface systems of inaccessible areas. Herein, a data-driven workflow for extracting the principal features using a digital terrain model (DTM) is proposed. In addition, geospatial grid information containing terrain features and the average shear wave velocity in the top 30 m of the subsurface (VS30) are employed using geostatistical interpolation methods; machine learning (ML)-based regression models were optimized and VS30-based seismic zonation in the test areas in North Korea were forecasted. The interrelationships between VS30 and terrain proxy (elevation, slope, and landform class) in the training area in South Korea were verified to define the input layer in regression models. The landform class represents a new proxy of VS30 and was subgrouped according to the correlation with grid-based VS30. The geospatial grid information was generated via the optimum geostatistical interpolation method (i.e., sequential Gaussian simulation (SGS)). The best-fitting model among four ML methods was determined by evaluating cost function-based prediction performance, performing uncertainty analysis for the empirical correlations of VS30, and studying spatial correspondence with the borehole-based VS30 map. Subsequently, the best-fitting regression models were designed by training the geospatial grid in South Korea. Then, DTM and its terrain features were constructed along with VS30 maps for three major cities (Pyongyang, Kaesong, and Nampo) in North Korea. A similar distribution of the VS30 grid obtained using SGS was shown in the multilayer perceptron-based VS30 map. Full article
(This article belongs to the Special Issue Spatial Modelling of Natural Hazards and Water Resources through Remote Sensing, GIS and Machine Learning Methods)
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21 pages, 5691 KiB  
Article
Surface Water Changes in Dongting Lake from 1975 to 2019 Based on Multisource Remote-Sensing Images
by Yan Peng, Guojin He, Guizhou Wang and Hongjuan Cao
Remote Sens. 2021, 13(9), 1827; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091827 - 07 May 2021
Cited by 13 | Viewed by 2543
Abstract
Dongting Lake plays an important role in water regulation and biodiversity protection, but it is vulnerable to climate change and human activities. To quantify surface water changes and factors driving them, long-term surface water variation in Dongting Lake was investigated using the multiple [...] Read more.
Dongting Lake plays an important role in water regulation and biodiversity protection, but it is vulnerable to climate change and human activities. To quantify surface water changes and factors driving them, long-term surface water variation in Dongting Lake was investigated using the multiple spectral indices method based on a decision tree classification for full time-series Landsat and MODIS datasets. Factors influencing surface water changes were explored by combining the annual maximum surface water and annual permanent occurrent water with meteorological and hydrological data. The results showed that both annual maximum surface water and annual permanent water decreased from 1975 to 2019 and the trends of rainfall and runoff at three outlets also changed. The annual maximum surface water surface of Dongting Lake increased during the 1990s due to high rainfall but decreased again after 2000. A significant change in both the hydrological stage and surface water sequence from 1986 to 2019 occurred in 2003, which coincided with the beginning of work to construct the Three Gorges Dam (TGD). The surface water decreased by about 360 km2 and runoff at the three outlets decreased by about 150 × 108 m3 after the impoundment of the TGD, which was likely the main cause of surface water changes after 2003. The area of surface water that changed from permanent water in the pre-TGD period into seasonally occurring water in the post-TGD periods is 209 km2, while the area of surface water that changed from seasonally occurring water in the pre-TGD period into permanent occurrent water in the post-TGD period is 31 km2. Meteorological elements and human activities have had a comprehensive impact on surface water changes in Dongting Lake. Rainfall and temperature account for about one-third of the influence on long-term changes of surface water, and rainfall is the main meteorological driving factor of surface water in the wet season, while temperature is the main factor in the dry season. Runoff at three outlets, four rivers and the Chenglingji explain about half of the change in surface water; the three outlets runoff is mainly affected by human activities and is the main hydrological driving factor of surface water. The monthly maximum surface water fluctuates regularly and Dongting Lake has a strong seasonal characteristic. Indeed, the seasonal changes are significantly altered when drought or flooding occurs, the causes of which are diverse and complex. Full article
(This article belongs to the Special Issue Spatial Modelling of Natural Hazards and Water Resources through Remote Sensing, GIS and Machine Learning Methods)
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22 pages, 37135 KiB  
Article
Landslide Susceptibility Mapping Using Rotation Forest Ensemble Technique with Different Decision Trees in the Three Gorges Reservoir Area, China
by Zhice Fang, Yi Wang, Gonghao Duan and Ling Peng
Remote Sens. 2021, 13(2), 238; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020238 - 12 Jan 2021
Cited by 30 | Viewed by 3284
Abstract
This study presents a new ensemble framework to predict landslide susceptibility by integrating decision trees (DTs) with the rotation forest (RF) ensemble technique. The proposed framework mainly includes four steps. First, training and validation sets are randomly selected according to historical landslide locations. [...] Read more.
This study presents a new ensemble framework to predict landslide susceptibility by integrating decision trees (DTs) with the rotation forest (RF) ensemble technique. The proposed framework mainly includes four steps. First, training and validation sets are randomly selected according to historical landslide locations. Then, landslide conditioning factors are selected and screened by the gain ratio method. Next, several training subsets are produced from the training set and a series of trained DTs are obtained by using a DT as a base classifier couple with different training subsets. Finally, the resultant landslide susceptibility map is produced by combining all the DT classification results using the RF ensemble technique. Experimental results demonstrate that the performance of all the DTs can be effectively improved by integrating them with the RF ensemble technique. Specifically, the proposed ensemble methods achieved the predictive values of 0.012–0.121 higher than the DTs in terms of area under the curve (AUC). Furthermore, the proposed ensemble methods are better than the most popular ensemble methods with the predictive values of 0.005–0.083 in terms of AUC. Therefore, the proposed ensemble framework is effective to further improve the spatial prediction of landslides. Full article
(This article belongs to the Special Issue Spatial Modelling of Natural Hazards and Water Resources through Remote Sensing, GIS and Machine Learning Methods)
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28 pages, 10688 KiB  
Article
Uncertainties Analysis of Collapse Susceptibility Prediction Based on Remote Sensing and GIS: Influences of Different Data-Based Models and Connections between Collapses and Environmental Factors
by Wenbin Li, Xuanmei Fan, Faming Huang, Wei Chen, Haoyuan Hong, Jinsong Huang and Zizheng Guo
Remote Sens. 2020, 12(24), 4134; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12244134 - 17 Dec 2020
Cited by 39 | Viewed by 4180
Abstract
To study the uncertainties of a collapse susceptibility prediction (CSP) under the coupled conditions of different data-based models and different connection methods between collapses and environmental factors, An’yuan County in China with 108 collapses is used as the study case, and 11 environmental [...] Read more.
To study the uncertainties of a collapse susceptibility prediction (CSP) under the coupled conditions of different data-based models and different connection methods between collapses and environmental factors, An’yuan County in China with 108 collapses is used as the study case, and 11 environmental factors are acquired by data analysis of Landsat TM 8 and high-resolution aerial images, using a hydrological and topographical spatial analysis of Digital Elevation Modeling in ArcGIS 10.2 software. Accordingly, 20 coupled conditions are proposed for CSP with five different connection methods (Probability Statistics (PSs), Frequency Ratio (FR), Information Value (IV), Index of Entropy (IOE) and Weight of Evidence (WOE)) and four data-based models (Analytic Hierarchy Process (AHP), Multiple Linear Regression (MLR), C5.0 Decision Tree (C5.0 DT) and Random Forest (RF)). Finally, the CSP uncertainties are assessed using the area under receiver operation curve (AUC), mean value, standard deviation and significance test, respectively. Results show that: (1) the WOE-based models have the highest AUC accuracy, lowest mean values and average rank, and a relatively large standard deviation; the mean values and average rank of all the FR-, IV- and IOE-based models are relatively large with low standard deviations; meanwhile, the AUC accuracies of FR-, IV- and IOE-based models are consistent but higher than those of the PS-based model. Hence, the WOE exhibits a greater spatial correlation performance than the other four methods. (2) Among all the data-based models, the RF model has the highest AUC accuracy, lowest mean value and mean rank, and a relatively large standard deviation. The CSP performance of the RF model is followed by the C5.0 DT, MLR and AHP models, respectively. (3) Under the coupled conditions, the WOE-RF model has the highest AUC accuracy, a relatively low mean value and average rank, and a high standard deviation. The PS-AHP model is opposite to the WOE-RF model. (4) In addition, the coupled models show slightly better CSP performances than those of the single data-based models not considering connect methods. The CSP performance of the other models falls somewhere in between. It is concluded that the WOE-RF is the most appropriate coupled condition for CSP than the other models. Full article
(This article belongs to the Special Issue Spatial Modelling of Natural Hazards and Water Resources through Remote Sensing, GIS and Machine Learning Methods)
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26 pages, 7267 KiB  
Article
Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments
by Wei Chen, Yunzhi Chen, Paraskevas Tsangaratos, Ioanna Ilia and Xiaojing Wang
Remote Sens. 2020, 12(23), 3854; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233854 - 25 Nov 2020
Cited by 57 | Viewed by 4443
Abstract
The main objective of the present study is to introduce a novel predictive model that combines evolutionary algorithms and machine learning (ML) models, so as to construct a landslide susceptibility map. Genetic algorithms (GA) are used as a feature selection method, whereas the [...] Read more.
The main objective of the present study is to introduce a novel predictive model that combines evolutionary algorithms and machine learning (ML) models, so as to construct a landslide susceptibility map. Genetic algorithms (GA) are used as a feature selection method, whereas the particle swarm optimization (PSO) method is used to optimize the structural parameters of two ML models, support vector machines (SVM) and artificial neural network (ANN). A well-defined spatial database, which included 335 landslides and twelve landslide-related variables (elevation, slope angle, slope aspect, curvature, plan curvature, profile curvature, topographic wetness index, stream power index, distance to faults, distance to river, lithology, and hydrological cover) are considered for the analysis, in the Achaia Regional Unit located in Northern Peloponnese, Greece. The outcome of the study illustrates that both ML models have an excellent performance, with the SVM model achieving the highest learning accuracy (0.977 area under the receiver operating characteristic curve value (AUC)), followed by the ANN model (0.969). However, the ANN model shows the highest prediction accuracy (0.800 AUC), followed by the SVM (0.750 AUC) model. Overall, the proposed ML models highlights the necessity of feature selection and tuning procedures via evolutionary optimization algorithms and that such approaches could be successfully used for landslide susceptibility mapping as an alternative investigation tool. Full article
(This article belongs to the Special Issue Spatial Modelling of Natural Hazards and Water Resources through Remote Sensing, GIS and Machine Learning Methods)
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25 pages, 9605 KiB  
Article
Ensemble of Machine-Learning Methods for Predicting Gully Erosion Susceptibility
by Subodh Chandra Pal, Alireza Arabameri, Thomas Blaschke, Indrajit Chowdhuri, Asish Saha, Rabin Chakrabortty, Saro Lee and Shahab. S. Band
Remote Sens. 2020, 12(22), 3675; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223675 - 10 Nov 2020
Cited by 56 | Viewed by 3834
Abstract
Gully formation through water-induced soil erosion and related to devastating land degradation is often a quasi-normal threat to human life, as it is responsible for huge loss of surface soil. Therefore, gully erosion susceptibility (GES) mapping is necessary in order to reduce the [...] Read more.
Gully formation through water-induced soil erosion and related to devastating land degradation is often a quasi-normal threat to human life, as it is responsible for huge loss of surface soil. Therefore, gully erosion susceptibility (GES) mapping is necessary in order to reduce the adverse effect of land degradation and diminishes this type of harmful consequences. The principle goal of the present research study is to develop GES maps for the Garhbeta I Community Development (C.D.) Block; West Bengal, India, by using a machine learning algorithm (MLA) of boosted regression tree (BRT), bagging and the ensemble of BRT-bagging with K-fold cross validation (CV) resampling techniques. The combination of the aforementioned MLAs with resampling approaches is state-of-the-art soft computing, not often used in GES evaluation. In further progress of our research work, here we used a total of 20 gully erosion conditioning factors (GECFs) and a total of 199 gully head cut points for modelling GES. The variables’ importance, which is responsible for gully erosion, was determined based on the random forest (RF) algorithm among the several GECFs used in this study. The output result of the model’s performance was validated through a receiver operating characteristics-area under curve (ROC-AUC), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) statistical analysis. The predicted result shows that the ensemble of BRT-bagging is the most well fitted for GES where AUC value in K-3 fold is 0.972, whereas the value of AUC in sensitivity, specificity, PPV and NPV is 0.94, 0.93, 0.96 and 0.93, respectively, in a training dataset, and followed by the bagging and BRT model. Thus, from the predictive performance of this research study it is concluded that the ensemble of BRT-Bagging can be applied as a new approach for further studies in spatial prediction of GES. The outcome of this work can be helpful to policy makers in implementing remedial measures to minimize damages caused by gully erosion. Full article
(This article belongs to the Special Issue Spatial Modelling of Natural Hazards and Water Resources through Remote Sensing, GIS and Machine Learning Methods)
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18 pages, 8061 KiB  
Article
A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping
by Viet-Ha Nhu, Phuong-Thao Thi Ngo, Tien Dat Pham, Jie Dou, Xuan Song, Nhat-Duc Hoang, Dang An Tran, Duong Phan Cao, İbrahim Berkan Aydilek, Mahdis Amiri, Romulus Costache, Pham Viet Hoa and Dieu Tien Bui
Remote Sens. 2020, 12(17), 2688; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12172688 - 20 Aug 2020
Cited by 47 | Viewed by 4591
Abstract
Flash flood is one of the most dangerous natural phenomena because of its high magnitudes and sudden occurrence, resulting in huge damages for people and properties. Our work aims to propose a state-of-the-art model for susceptibility mapping of the flash flood using the [...] Read more.
Flash flood is one of the most dangerous natural phenomena because of its high magnitudes and sudden occurrence, resulting in huge damages for people and properties. Our work aims to propose a state-of-the-art model for susceptibility mapping of the flash flood using the decision tree random subspace ensemble optimized by hybrid firefly–particle swarm optimization (HFPS), namely the HFPS-RSTree model. In this work, we used data from a flood inventory map consisting of 1866 polygons derived from Sentinel-1 C-band synthetic aperture radar (SAR) data and a field survey conducted in the northwest mountainous area of the Van Ban district, Lao Cai Province in Vietnam. A total of eleven flooding conditioning factors (soil type, geology, rainfall, river density, elevation, slope, aspect, topographic wetness index (TWI), normalized difference vegetation index (NDVI), plant curvature, and profile curvature) were used as explanatory variables. These indicators were compiled from a geological and mineral resources map, soil type map, and topographic map, ALOS PALSAR DEM 30 m, and Landsat-8 imagery. The HFPS-RSTree model was trained and verified using the inventory map and the eleven conditioning variables and then compared with four machine learning algorithms, i.e., the support vector machine (SVM), the random forests (RF), the C4.5 decision trees (C4.5 DT), and the logistic model trees (LMT) models. We employed a range of statistical standard metrics to assess the predictive performance of the proposed model. The results show that the HFPS-RSTree model had the best predictive performance and achieved better results than those of other benchmarks with the ability to predict flash flood, reaching an overall accuracy of over 90%. It can be concluded that the proposed approach provides new insights into flash flood prediction in mountainous regions. Full article
(This article belongs to the Special Issue Spatial Modelling of Natural Hazards and Water Resources through Remote Sensing, GIS and Machine Learning Methods)
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25 pages, 4687 KiB  
Article
GIS-Based Machine Learning Algorithms for Gully Erosion Susceptibility Mapping in a Semi-Arid Region of Iran
by Xinxiang Lei, Wei Chen, Mohammadtaghi Avand, Saeid Janizadeh, Narges Kariminejad, Hejar Shahabi, Romulus Costache, Himan Shahabi, Ataollah Shirzadi and Amir Mosavi
Remote Sens. 2020, 12(15), 2478; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12152478 - 02 Aug 2020
Cited by 91 | Viewed by 7123
Abstract
In the present study, gully erosion susceptibility was evaluated for the area of the Robat Turk Watershed in Iran. The assessment of gully erosion susceptibility was performed using four state-of-the-art data mining techniques: random forest (RF), credal decision trees (CDTree), kernel logistic regression [...] Read more.
In the present study, gully erosion susceptibility was evaluated for the area of the Robat Turk Watershed in Iran. The assessment of gully erosion susceptibility was performed using four state-of-the-art data mining techniques: random forest (RF), credal decision trees (CDTree), kernel logistic regression (KLR), and best-first decision tree (BFTree). To the best of our knowledge, the KLR and CDTree algorithms have been rarely applied to gully erosion modeling. In the first step, from the 242 gully erosion locations that were identified, 70% (170 gullies) were selected as the training dataset, and the other 30% (72 gullies) were considered for the result validation process. In the next step, twelve gully erosion conditioning factors, including topographic, geomorphological, environmental, and hydrologic factors, were selected to estimate gully erosion susceptibility. The area under the ROC curve (AUC) was used to estimate the performance of the models. The results revealed that the RF model had the best performance (AUC = 0.893), followed by the KLR (AUC = 0.825), the CDTree (AUC = 0.808), and the BFTree (AUC = 0.789) models. Overall, the RF model performed significantly better than the others, which may support the application of this method to a transferable susceptibility model in other areas. Therefore, we suggest using the RF, KLR, and CDT models for gully erosion susceptibility mapping in other prone areas to assess their reproducibility. Full article
(This article belongs to the Special Issue Spatial Modelling of Natural Hazards and Water Resources through Remote Sensing, GIS and Machine Learning Methods)
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27 pages, 20772 KiB  
Article
Optimization of Computational Intelligence Models for Landslide Susceptibility Evaluation
by Xia Zhao and Wei Chen
Remote Sens. 2020, 12(14), 2180; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12142180 - 08 Jul 2020
Cited by 97 | Viewed by 3888
Abstract
This paper focuses on landslide susceptibility prediction in Nanchuan, a high-risk landslide disaster area. The evidential belief function (EBF)-based function tree (FT), logistic regression (LR), and logistic model tree (LMT) were applied to Nanchuan District, China. Firstly, an inventory with 298 landslides was [...] Read more.
This paper focuses on landslide susceptibility prediction in Nanchuan, a high-risk landslide disaster area. The evidential belief function (EBF)-based function tree (FT), logistic regression (LR), and logistic model tree (LMT) were applied to Nanchuan District, China. Firstly, an inventory with 298 landslides was compiled and separated into two parts (70%: 209; 30%: 89) as training and validation datasets. Then, based on the EBF method, the Bel values of 16 conditioning factors related to landslide occurrence were calculated, and these Bel values were used as input data for building other models. The receiver operating characteristic (ROC) curve and the values of the area under the ROC curve (AUC) were used to evaluate and compare the prediction ability of the four models. All the models achieved good results and performed well. In particular, the LMT model had the best performance (0.847 and 0.765, obtained from the training and validation datasets, respectively). This paper also demonstrates the superiority of integration and optimization of models in landslide susceptibility evaluation. Finally, the best classification method was selected to draw landslide susceptibility maps, which may be helpful for government administrators and engineers to carry out land design and planning. Full article
(This article belongs to the Special Issue Spatial Modelling of Natural Hazards and Water Resources through Remote Sensing, GIS and Machine Learning Methods)
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