Flood Susceptibility Prediction Using Hybrid-Based Approaches of Support Vector Regression Model and Meta-Heuristic Algorithms

Abstract Landslides are one of the most destructive natural phenomena in the world, which occur mostly in mountainous areas and cause damage to the economic sectors, agricultural lands, residential areas and infrastructures of any country, and also threaten the lives and property of human beings. Therefore, landslide susceptibility mapping (LSM) can play a critical role in identifying prone areas and reducing the damage caused by landslides in each area. In the present study, deep learning algorithms including convolutional neural network (CNN) and long short-term memory (LSTM) were used to identify landslide prone areas in Ardabil province, Iran. Equql to 312 landslide locations were identified and randomly divided into train and test datasets at 70–30% ratios. Then, according to previous studies and environmental conditions in the study area, twelve factors affecting the occurrence of landslides were selected, namely altitude, slope angle, slope aspect, topographic wetness index (TWI), profile curvature, plan curvature, land-use, lithology, distance to faults, distance to rivers, distance to roads, and rainfall. The ratio of the importance of each influential factor in landslide occurrence was obtained through information gain ranking filter (IGRF) method and it was found that land-use and profile curvature had the highest and lowest impacts, respectively. Afterwards, LSMs were generated using CNN and LSTM algorithms. In the next step, the performance of the models was evaluated based on the area under curve (AUC) value of receiver operating characteristics curve and the root mean square error (RMSE) method. The AUC values for CNN and LSTM models were 0.821 and 0.832, respectively. Furthermore, the RMSE values in the CNN model for each of the training and testing dataset were 0.121 and 0.132, respectively. The RMSE values in the LSTM model for each of the training and testing dataset were 0.185 and 0.188, respectively. Therefore, it can be concluded that CNN performance is slightly better than LSTM; but in general, both models have close performance and the accuracy of both models is acceptable.

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Gully Erosion Susceptibility Assessment in the Kondoran Watershed Using Machine Learning Algorithms and the Boruta Feature Selection

Sustainability ◽

10.3390/su131810110 ◽

2021 ◽

Vol 13 (18) ◽

pp. 10110

Author(s):

Hamed Ahmadpour ◽

Ommolbanin Bazrafshan ◽

Elham Rafiei-Sardooi ◽

Hossein Zamani ◽

Thomas Panagopoulos

Keyword(s):

Gully Erosion ◽

Susceptibility Mapping ◽

Management Tool ◽

Slope Aspect ◽

Support Vector ◽

Ensemble Model ◽

Operating Characteristics ◽

Topographic Wetness Index ◽

Study Region ◽

Receiver Operating

Gully erosion susceptibility mapping is an essential land management tool to reduce soil erosion damages. This study investigates gully susceptibility based on multiple diagnostic analysis, support vector machine and random forest algorithms, and also a combination of these models, namely the ensemble model. Thus, a gully susceptibility map in the Kondoran watershed of Iran was generated by applying these models on the occurrence and non-occurrence points (as the target variable) and several predictors (slope, aspect, elevation, topographic wetness index, drainage density, plan curvature, distance to streams, lithology, soil texture and land use). The Boruta algorithm was used to select the most effective variables in modeling gully erosion susceptibility. The area under the receiver operating characteristic curve (AUC), the receiver operating characteristics, and true skill statistics (TSS) were used to assess the model performance. The results indicated that the ensemble model had the best performance (AUC = 0.982, TSS = 0.93) compared to the others. The most effective factors in gully erosion susceptibility mapping of the study region were topological, anthropogenic, and geological. The methodology and variables of this study can be used in other regions to control and mitigate the gully erosion phenomenon by applying biophilic and regenerative techniques at the locations of the most influential factors.

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A COMPARISON BETWEEN THREE CONDITIONING FACTORS DATASET FOR LANDSLIDE PREDICTION IN THE SAJADROOD CATCHMENT OF IRAN

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-3-2020-625-2020 ◽

2020 ◽

Vol V-3-2020 ◽

pp. 625-632

Author(s):

B. Kalantar ◽

N. Ueda ◽

H. A. H. Al-Najjar ◽

V. Saeidi ◽

M. B. A. Gibril ◽

...

Keyword(s):

Land Use ◽

Total Curvature ◽

The Other ◽

Slope Aspect ◽

Support Vector ◽

Topographic Wetness Index ◽

Mazandaran Province ◽

Landslide Prediction ◽

Conditioning Factors ◽

Other Hand

Abstract. This study investigates the effectiveness of three datasets for the prediction of landslides in the Sajadrood catchment (Babol County, Mazandaran Province, Iran). The three datasets (D1, D2 and D3) are constructed based on fourteen conditioning factors (CFs) obtained from Digital Elevation Model (DEM) derivatives, topography maps, land use maps and geological maps. Precisely, D1 consists of all 14 CFs namely altitude, slope, aspect, topographic wetness index (TWI), terrain roughness index (TRI), distance to fault, distance to stream, distance to road, total curvature, profile curvatures, plan curvature, land use, steam power index (SPI) and geology. D2, on the other hand, is a subset of D1, consisting of eight CFs. This reduction was achieved by exploiting the Variance Inflation Factor, Gini Importance Indices and Chi-Square factor optimization methods. Dataset D3 includes only selected factors derived from the DEM. Three supervised classification algorithms were trained for landslide prediction namely the Support Vector Machine (SVM), Logistic Regression (LR), and Artificial Neural Network (ANN). Experimental results indicate that D2 performed the best for landslide prediction with the SVM producing the best overall accuracy at 82.81%, followed by LR (81.71%) and ANN (80.18%). Extensive investigations on the results of factor optimization analysis indicate that the CFs distance to road, altitude, and geology were significant contributors to the prediction results. Land use map, slope, total-, plan-, and profile curvature and TRI, on the other hand, were deemed redundant. The analysis also revealed that sole reliance on Gini Indices could lead to inefficient optimization.

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A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia

Sensors ◽

10.3390/s19224893 ◽

2019 ◽

Vol 19 (22) ◽

pp. 4893 ◽

Cited By ~ 22

Author(s):

Hejar Shahabi ◽

Ben Jarihani ◽

Sepideh Tavakkoli Piralilou ◽

David Chittleborough ◽

Mohammadtaghi Avand ◽

...

Keyword(s):

Machine Learning ◽

Image Segmentation ◽

Vegetation Index ◽

Scale Parameter ◽

Slope Aspect ◽

Support Vector ◽

Topographic Wetness Index ◽

Slope Length ◽

Optimal Scale ◽

Object Based

Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected the Bowen catchment, a tributary of the Burdekin Basin, as our area of study; the region is associated with a high density of gully networks. We aimed to use a semi-automated object-based gully networks detection process using a combination of multi-source and multi-scale remote sensing and ground-based data. An advanced approach was employed by integrating geographic object-based image analysis (GEOBIA) with current machine learning (ML) models. These included artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), and an ensemble ML model of stacking to deal with the spatial scaling problem in gully networks detection. Spectral indices such as the normalized difference vegetation index (NDVI) and topographic conditioning factors, such as elevation, slope, aspect, topographic wetness index (TWI), slope length (SL), and curvature, were generated from Sentinel 2A images and the ALOS 12-m digital elevation model (DEM), respectively. For image segmentation, the ESP2 tool was used to obtain three optimal scale factors. On using object pureness index (OPI), object matching index (OMI), and object fitness index (OFI), the accuracy of each scale in image segmentation was evaluated. The scale parameter of 45 with OFI of 0.94, which is a combination of OPI and OMI indices, proved to be the optimal scale parameter for image segmentation. Furthermore, segmented objects based on scale 45 were overlaid with 70% and 30% of a prepared gully inventory map to select the ML models’ training and testing objects, respectively. The quantitative accuracy assessment methods of Precision, Recall, and an F1 measure were used to evaluate the model’s performance. Integration of GEOBIA with the stacking model using a scale of 45 resulted in the highest accuracy in detection of gully networks with an F1 measure value of 0.89. Here, we conclude that the adoption of optimal scale object definition in the GEOBIA and application of the ensemble stacking of ML models resulted in higher accuracy in the detection of gully networks.

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Testing PRISMA hyperspectral satellite imagery in predicting soil carbon content based on synthetized LUCAS spectral data

10.5194/egusphere-egu21-15450 ◽

2021 ◽

Author(s):

Zsófia Adrienn Kovács ◽

János Mészáros ◽

Mátyás Árvai ◽

Annamária Laborczi ◽

Gábor Szatmári ◽

...

Keyword(s):

Land Use ◽

Spectral Data ◽

Hyperspectral Imagery ◽

Slope Aspect ◽

Topographic Wetness Index ◽

Soil Carbon Content ◽

Space Agency ◽

Italian Space Agency ◽

Agricultural Areas ◽

Imagery Data

The estimation of the soil organic carbon (SOC) content plays an important role for carbon sequestration in the context of climate change and soil degradation. Reflectance spectroscopy has proven to be promising technique for SOC quantification in the laboratory and increasingly from air and spaceborne platforms, where hyperspectral imagery provides great potential for mapping SOC on larger scales.The PRISMA (PRecursore IperSpettrale della Missione Applicativa) is an earth-observation satellite with a medium spatial resolution hyperspectral radiometer onboard, developed and maintained by the Italian Space Agency.The Pan-European Land Use/ Land Cover Area Frame Survey (LUCAS) topsoil database contains soil physical, chemical and spectral data for most European countries. Based on the LUCAS points located in Hungary, a synthetized spectral dataset was created and matched to the spectral characteristic of PRISMA sensor, later used for building up machine learning based models (random forest, artificial neural network). SOC levels for the sample area was predicted using generated models and mainly PRISMA imagery.Our sample imagery data was generated from five consecutive, cloud-free PRISMA images covering 4500 km2 in the central part of the Great Plain in Hungary, which is one of the most important agricultural areas of the country, used mainly for crops on arable lands. The images were recorded in 2020 February when most croplands are not covered by vegetation therefore our tests were implemented on bare soils.We tested the prediction accuracy of hyperspectral imagery data supplemented by various environmental datasets as additional predictor variables in four scenarios: (i) using solely hyperspectral imagery data (ii) spectral imagery data, elevation and its derived parameters (e.g. slope, aspect, topographic wetness index etc.) (iii) spectral imagery data and land-use information and (iv) all aforementioned data in fusion.For validation two types of datasets were used: (i) measured data at the observation sites of the Hungarian Soil Information and Monitoring System and (ii) the recently compiled national SOC maps., which provides a suitable and formerly tested spatial representation of the carbon stock of the Hungarian soils.&#160;Acknowledgment: Our research was supported by the Cooperative Doctoral Programme for Doctoral Scholarships (1015642) and by the OTKA thematic research projects K-131820 and K-124290 of the Hungarian National Research, Development and Innovation Office and by the Scholarship of Human Resource Supporter (NTP-NFT&#214;-20-B-0022). Our project carried out using PRISMA Products, &#169; of the Italian Space Agency (ASI), delivered under an ASI License to use.

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Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling

Entropy ◽

10.3390/e20110884 ◽

2018 ◽

Vol 20 (11) ◽

pp. 884 ◽

Cited By ~ 28

Author(s):

Tingyu Zhang ◽

Ling Han ◽

Wei Chen ◽

Himan Shahabi

Keyword(s):

Support Vector Machine ◽

Logistic Regression ◽

Landslide Susceptibility ◽

Shaanxi Province ◽

Slope Aspect ◽

Support Vector ◽

Operating Characteristics ◽

Study Region ◽

Susceptibility Maps ◽

Landslide Susceptibility Maps

The main purpose of the present study is to apply three classification models, namely, the index of entropy (IOE) model, the logistic regression (LR) model, and the support vector machine (SVM) model by radial basis function (RBF), to produce landslide susceptibility maps for the Fugu County of Shaanxi Province, China. Firstly, landslide locations were extracted from field investigation and aerial photographs, and a total of 194 landslide polygons were transformed into points to produce a landslide inventory map. Secondly, the landslide points were randomly split into two groups (70/30) for training and validation purposes, respectively. Then, 10 landslide explanatory variables, such as slope aspect, slope angle, altitude, lithology, mean annual precipitation, distance to roads, distance to rivers, distance to faults, land use, and normalized difference vegetation index (NDVI), were selected and the potential multicollinearity problems between these factors were detected by the Pearson Correlation Coefficient (PCC), the variance inflation factor (VIF), and tolerance (TOL). Subsequently, the landslide susceptibility maps for the study region were obtained using the IOE model, the LR–IOE, and the SVM–IOE model. Finally, the performance of these three models was verified and compared using the receiver operating characteristics (ROC) curve. The success rate results showed that the LR–IOE model has the highest accuracy (90.11%), followed by the IOE model (87.43%) and the SVM–IOE model (86.53%). Similarly, the AUC values also showed that the prediction accuracy expresses a similar result, with the LR–IOE model having the highest accuracy (81.84%), followed by the IOE model (76.86%) and the SVM–IOE model (76.61%). Thus, the landslide susceptibility map (LSM) for the study region can provide an effective reference for the Fugu County government to properly address land planning and mitigate landslide risk.

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Production of Landslide Susceptibility Map using Bayesian Probability Model

International Journal of 3-D Information Modeling ◽

10.4018/ij3dim.2015040102 ◽

2015 ◽

Vol 4 (2) ◽

pp. 16-33 ◽

Cited By ~ 1

Author(s):

Halil Akıncı ◽

Ayşe Yavuz Özalp ◽

Mehmet Özalp ◽

Sebahat Temuçin Kılıçer ◽

Cem Kılıçoğlu ◽

...

Keyword(s):

Land Use ◽

Land Use Planning ◽

Landslide Susceptibility ◽

Probability Model ◽

Bayesian Probability ◽

Slope Aspect ◽

Susceptibility Map ◽

Model Parameters ◽

Operating Characteristics ◽

Landslide Susceptibility Map

Artvin is one of the provinces in Turkey where landslides occur most frequently. There have been numerous landslides characterized as natural disaster recorded across the province. The areas sensitive to landslides across the province should be identified in order to ensure people's safety, to take the necessary measures for reducing any devastating effects of landslides and to make the right decisions in respect to land use planning. In this study, the landslide susceptibility map of the Central district of Artvin was produced by using Bayesian probability model. Parameters including lithology, altitude, slope, aspect, plan and profile curvatures, soil depth, topographic wetness index, land cover, and proximity to the road and stream were used in landslide susceptibility analysis. The landslide susceptibility map produced in this study was validated using the receiver operating characteristics (ROC) based on area under curve (AUC) analysis. In addition, control landslide locations were used to validate the results of the landslide susceptibility map and the validation analysis resulted in 94.30% accuracy, a reliable outcome for this map that can be useful for general land use planning in Artvin.

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Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data

Sensors ◽

10.3390/s19163451 ◽

2019 ◽

Vol 19 (16) ◽

pp. 3451 ◽

Cited By ~ 8

Author(s):

Usman Salihu Lay ◽

Biswajeet Pradhan ◽

Zainuddin Bin Md Yusoff ◽

Ahmad Fikri Bin Abdallah ◽

Jagannath Aryal ◽

...

Keyword(s):

Data Mining ◽

Debris Flow ◽

Airborne Lidar ◽

Multivariate Adaptive Regression Splines ◽

Slope Aspect ◽

Support Vector ◽

Mountainous Area ◽

Topographic Wetness Index ◽

Conditioning Factors ◽

Debris Flow Susceptibility

Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer’s V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.

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Optimization of random forest model for assessing and predicting geological hazards susceptibility in Lingyun County

10.21203/rs.3.rs-231323/v1 ◽

2021 ◽

Author(s):

Chunfang Kong ◽

Junzuo Wang ◽

Xiaogang Ma ◽

Yiping Tian ◽

Zhiting Zhang ◽

...

Keyword(s):

Land Use ◽

Random Forest ◽

Vegetation Index ◽

Evaluation Model ◽

Field Investigation ◽

Slope Aspect ◽

Geological Hazards ◽

Operating Characteristics ◽

Model Based ◽

Prediction And Prevention

Abstract The frequent occurrence of geological hazards will not only cause peoples' property loss and deterioration of living environments, but will also endanger peoples' lives. Therefore, rapid and accurate evaluation of geological hazards susceptibility can provide an important scientific basis for emergency rescue and disaster reduction and prevention. In this paper, ten effective variables including slope, aspect, curvature, normalized differential vegetation index, annual precipitation, strata lithology, tectonic complexity, residential density, road network density, and land use/land cover were selected as evaluation indexes. Meanwhile, random forest (RF) model is improved by the optimization of unbalanced geological hazards dataset, differentiation of continuous geological hazards evaluation factors, sample similarity calculation, and iterative method for finding optimal random characteristics by calculating out-of-bagger errors. The geological hazards susceptibility evaluation model based on optimized RF (OPRF) was established and used to assess the susceptibility level of geological hazards for Lingyun County. Then, receiver operating characteristics (ROC) curves and field investigation were performed to verify the efficiency for five models. Analysis and comparison of the results denoted that the model based on OPRF has the highest prediction accuracy of 93.4%, which is far better than the other four models. Furthermore, the evaluation results can provide reference for geological hazards prediction and prevention, and can also provide decision support for land use development and rational utilization of resources and environment in Lingyun County. Based on these results, the OPRF model could be extended to other regions with similar geological environment backgrounds for geological hazards susceptibility assessment and prediction.

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Evaluation of the Effect of Hydroseeded Vegetation for Slope Reinforcement

Land ◽

10.3390/land10100995 ◽

2021 ◽

Vol 10 (10) ◽

pp. 995

Author(s):

Okoli Jude Emeka ◽

Haslinda Nahazanan ◽

Bahareh Kalantar ◽

Zailani Khuzaimah ◽

Ojogbane Success Sani

Keyword(s):

Landslide Susceptibility ◽

Ground Cover ◽

Slope Aspect ◽

Susceptibility Map ◽

Water Runoff ◽

Operating Characteristics ◽

Topographic Wetness Index ◽

Landslide Susceptibility Map ◽

Ahp Model ◽

Slope Reinforcement

A landslide is a significant environmental hazard that results in an enormous loss of lives and properties. Studies have revealed that rainfall, soil characteristics, and human errors, such as deforestation, are the leading causes of landslides, reducing soil water infiltration and increasing the water runoff of a slope. This paper introduces vegetation establishment as a low-cost, practical measure for slope reinforcement through the ground cover and the root of the vegetation. This study reveals the level of complexity of the terrain with regards to the evaluation of high and low stability areas and has produced a landslide susceptibility map. For this purpose, 12 conditioning factors, namely slope, aspect, elevation, curvature, hill shade, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distances to roads, distance to lakes, distance to trees, and build-up, were used through the analytic hierarchy process (AHP) model to produce landslide susceptibility map. Receiver operating characteristics (ROC) was used for validation of the results. The area under the curve (AUC) values obtained from the ROC method for the AHP model was 0.865. Four seed samples, namely ryegrass, rye corn, signal grass, and couch, were hydroseeded to determine the vegetation root and ground cover’s effectiveness on stabilization and reinforcement on a high-risk susceptible 65° slope between August and December 2020. The observed monthly vegetation root of couch grass gave the most acceptable result. With a spreading and creeping vegetation ground cover characteristic, ryegrass showed the most acceptable monthly result for vegetation ground cover effectiveness. The findings suggest that the selection of couch species over other species is justified based on landslide control benefits.

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Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments

Remote Sensing ◽

10.3390/rs12233854 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3854 ◽

Cited By ~ 1

Author(s):

Wei Chen ◽

Yunzhi Chen ◽

Paraskevas Tsangaratos ◽

Ioanna Ilia ◽

Xiaojing Wang

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Evolutionary Algorithms ◽

Landslide Susceptibility ◽

Structural Parameters ◽

Slope Angle ◽

Slope Aspect ◽

Support Vector ◽

Ann Model ◽

Topographic Wetness Index

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.

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