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.

https://www.mdpi.com/2072-4292/12/15/2478/pdf

GIS-Based Machine Learning Algorithms for Gully Erosion Susceptibility Mapping in a Semi-Arid Region of Iran

In this study, we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models. We created a geographic information system database, and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth, aerial photographs, and other validated sources. A support vector regression (SVR) machine-learning model was used to divide the landslide inventory into training (70%) and testing (30%) datasets. The landslide susceptibility map was produced using 14 causative factors. We applied the established gray wolf optimization (GWO) algorithm, bat algorithm (BA), and cuckoo optimization algorithm (COA) to fine-tune the parameters of the SVR model to improve its predictive accuracy. The resultant hybrid models, SVR-GWO, SVR-BA, and SVR-COA, were validated in terms of the area under curve (AUC) and root mean square error (RMSE). The AUC values for the SVR-GWO (0.733), SVR-BA (0.724), and SVR-COA (0.738) models indicate their good prediction rates for landslide susceptibility modeling. SVR-COA had the greatest accuracy, with an RMSE of 0.21687, and SVR-BA had the least accuracy, with an RMSE of 0.23046. The three optimized hybrid models outperformed the SVR model (AUC = 0.704, RMSE = 0.26689), confirming the ability of metaheuristic algorithms to improve model performance.

Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms

Landslides are dangerous events which threaten both human life and property. The study aims to analyze the landslide susceptibility (LS) in the Kysuca river basin, Slovakia. For this reason, previo...

A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping

Landslide hazard assessment is critical for preventing and mitigating landslide disasters. The tuning of hyperparameters is of great importance to achieve better accuracy in a landslide hazard assessment model. In this study, a novel approach is proposed for landslide hazard assessment with support vector machine (SVM) as the primary model and Bayesian optimization (BO) algorithm as the parameter tuning method. This study describes 1711 historical landslide disaster points in Nanping City, and a total of 12 landslide conditioning factors including elevation, slope, aspect, curvature, lithology, soil type, soil erosion, rainfall, river, land use, highway, and railway were selected. The multicollinearity diagnosis was performed on the factors using the Spearman correlation coefficient. For model validation, 1711 landslides and 1711 non-landslides were collected as the dataset and divided into a training dataset (50 %) and a testing dataset (50 %). The performance of the model was evaluated by the confusion matrix and receiver operating characteristic (ROC) curve. The results of the confusion matrix accuracy and the area under the ROC curve showed that the BO-SVM model (89.53 %, 0.97) performed better than the SVM model (84.91 %, 0.93). In addition, the landslide hazard maps generated by the BO-SVM model had better overall results than that by the SVM model.

/pdf/landslide-hazard-assessment-based-on-bayesian-optimization-5gfnhka7av.pdf

Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, China

The success rate in landslide susceptibility mapping efforts increased with the advancements in machine learning algorithms and the availability of geospatial data with high spatial and temporal resolutions. Existing data-driven susceptibility mapping models are not globally applicable due to the high variability of landslide conditioning parameters and the limitations in the availability of up-to-date and accurate data. Among numerous applications, landslide susceptibility maps are essential for site selection and health monitoring of engineering structures, such as dams, for increasing their lifetime and to prevent from disastrous events caused by the damages. In this study, landslide susceptibility mapping performance of XGBoost algorithm was evaluated in a landslide-prone area in the upper basin of Ataturk Dam, which is a prime investment located in the southeast of Turkey. The study area has a size of 2718.7 km2 with an elevation difference of ca. 2000 m and contains 27 lithological units. EU-DEM v1.1 from the Copernicus Programme was used to derive the geomorphological features. High classification accuracy with area under curve value of 0.96 could be obtained from the XGBoost algorithm. According to the results, the main factors controlling the landslides in the study area are the lithology, altitude and topographic wetness index.

A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey

Urban areas may be affected by multiple hazards, and integrated hazard susceptibility maps are needed for suitable site selection and planning. Furthermore, geological–geotechnical parameters, construction costs, and the spatial distribution of existing infrastructure should be taken into account for this purpose. Up-to-date land-use and land-cover (LULC) maps, as well as natural hazard susceptibility maps, can be frequently obtained from high-resolution satellite sensors. In this study, an integrated hazard susceptibility assessment was performed for a developing urban settlement (Mamak District of Ankara City, Turkey) considering landslide and flood potential. The flood susceptibility map of Ankara City was produced in a previous study using modified analytical hierarchical process (M-AHP) approach. The landslide susceptibility map was produced using the logistic regression technique in this study. Sentinel-2 images were employed for generating LULC data with the random forest classification method. Topographical derivatives obtained from a high-resolution digital elevation model and lithological parameters were employed for the production of landslide susceptibility maps. For the integrated hazard susceptibility assessment, the Mamdani fuzzy algorithm was considered, and the results are discussed in the present study. The results demonstrate that multi-hazard susceptibility assessment maps for urban planning can be obtained by combining a set of expert-based and ensemble learning methods.

Use of Mamdani Fuzzy Algorithm for Multi-Hazard Susceptibility Assessment in a Developing Urban Settlement (Mamak, Ankara, Turkey)

This study aims to understand the reasons behind people's modal preferences in commuting trips, which contribute to transportation-related problems and to develop and prioritize policies that increase the use of sustainable modes. For this reason, in this study, a comparative case study was conducted to evaluate the factors that lead to choosing or not choosing sustainable modes. Commuting trips in San Francisco, San Diego, Los Angeles, San Jose and Sacramento, which are large cities with high automobile dependency and many potential alternatives to driving, were examined using the National Household Transportation Survey (NHTS) and spatial data. The factor classifications suggested in the literature were shaped according to the scope of the study and examined on four main headings. Proximity analyses were completed in the GIS environment to evaluate the environmental effects. The effects of all factor parameters on modal preferences were calculated for each mode of transport in the study with Binary Logistic Regression analyses. As a result of the comparative analysis, the barriers to using sustainable transportation modes in cities and the reasons for driving are determined. As a result, long-term policies were determined and prioritized with literature to manage these causes and barriers. The results show that modal choices are affected by many different parameters, the reasons differ locationally, and accordingly, the policy measures in each region should be different. 

Understanding the Choice for Sustainable Modes of Transport in Commuting Trips with A Comparative Case Study

This article provides a review of the general characteristics of Informal Public Transport (IPT) systems, the reasons behind the increasing use of these systems in developing countries, and the evaluation of Bus Rapid Transit Systems (BRTS) as an alternative solution to IPTs. IPTs, whose use has increased significantly in many countries where Formal Public Transport is insufficient, are different in their service, operation, finance, monitoring and labour features. These systems' usage has increased in developing countries for many economic, social and political reasons such as low income, rapid population growth, increase in demand, urban macroform, job opportunities and more flexible routes. However, besides the positive effects of these systems, there are many adverse effects. It is challenging to transfer the public transport schemes made in developed countries directly to these countries, and understanding the reasons of current situation in detail allows to offer the right solution. Nevertheless, BRTS, which are segregated busways, is seen as a solution in many countries, and there are many agreeing and disagreeing arguments for this idea in the literature. Although there are examples where these systems improve public transport systems, there are many examples where they do not contribute to the solution. As a result, it has been reached that the diversity of the reasons for the increase in the use of IPT systems is high, and BRTS can be an alternative, producing spatially, economically, environmentally and politically appropriate solutions according to the local characteristics of the countries can be more beneficial than a single solution.

An Examination of the Rise of Informal Public Transport Systems in Developing Countries and the Critiques About Bus Rapid Transit Systems as a Desired Solution

Tugce Yanar

Papers

Use of Mamdani Fuzzy Algorithm for Multi-Hazard Susceptibility Assessment in a Developing Urban Settlement (Mamak, Ankara, Turkey)

Understanding the Choice for Sustainable Modes of Transport in Commuting Trips with A Comparative Case Study

An Examination of the Rise of Informal Public Transport Systems in Developing Countries and the Critiques About Bus Rapid Transit Systems as a Desired Solution