Alireza Motevalli

Bio: Alireza Motevalli is an academic researcher from Tarbiat Modares University. The author has contributed to research in topics: Landslide & Topographic Wetness Index. The author has an hindex of 8, co-authored 13 publications receiving 484 citations. Previous affiliations of Alireza Motevalli include Lund University.

Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)

Abstract: Landslide is a natural hazard that results in many economic damages and human losses every year. Numerous researchers have studied landslide susceptibility mapping (LSM), each attempting to improve the accuracy of the final outputs. However, few studies have been published on the training data selection effects on the LSM. Thus, this study assesses the training landslides random selection effects on support vector machine (SVM) accuracy, logistic regression (LR) and artificial neural networks (ANN) models for LSM in a catchment at the Dodangeh watershed, Mazandaran province, Iran. A 160 landslide locations inventory was collected by Geological Survey of Iran for this investigation. Different methods were implemented to define the landslide locations, such as inventory reports, satellite images and field survey. Moreover, 14 landslide conditioning factors were considered in the analysis of landslide susceptibility. These factors include curvature, plan curvature, profile curvature, altitude, slope ...

Spatial modelling of gully erosion in Mazandaran Province, northern Iran

Abstract: Gully erosion is one of the most severe environmental problems in large areas of Iran. The spatial distribution of gully erosion and its susceptibility zonation was studied using different bivariate statistical models, such as frequency ratio (FR), weights of evidence (WofE), and index of entropy (IofE). For this purpose, 109 gully erosion locations were identified and divided into training (70%) and validating (30%) datasets. Effective factors, including elevation, slope aspect, slope degree, slope-length (LS), topographical wetness index (TWI), plan curvature, profile curvature, land use, lithology, distance from river, drainage density, and distance from road were selected to develop maps of gully erosion susceptibility. The spatial relationship between gully erosion and each effective factor was calculated by the mentioned models. The relative operating characteristic (ROC) curve was implemented for evaluating the accuracy of the applied predictive models. Results indicated that frequency ratio model had better performance (80.4%) than the weight of evidence (79.5%) and index of entropy (79%) models. The produced gully erosion susceptibility maps can be helpful to make decisions for soil and water planning and management and finally sustainable development in the Valasht watershed.

Gully Erosion Modeling Using GIS-Based Data Mining Techniques in Northern Iran: A Comparison Between Boosted Regression Tree and Multivariate Adaptive Regression Spline

Abstract: Land degradation occurs in the form of soil erosion in many regions of the world. Among the different type of soil erosion, high sediment yield volume in the watersheds is allocated to gully erosion. So, the purpose of this research is to map the susceptibility of the Valasht Watershed in northern Iran (Mazandaran Province) to gully erosion. For this purpose, spatial distribution of gullies was digitized by sampling and field monitoring. Identified gullies were divided into a training (two-thirds) and validating (one-third) datasets. In the second step, eleven effective factors including topographic (elevation, aspect, slope degree, TWI, plan curvature, and profile curvature), hydrologic (distance from river and drainage density), man-made (land use, distance from roads), and lithology factors were considered for spatial modeling of gully erosion. Then, Boosted Regression Tree (BRT) and Multivariate Adaptive Regression Spline (MARS) algorithms were implemented to model gully erosion susceptibility. Finally, Receiver Operating Characteristic (ROC) used for the assessment of prepared models. Based on the findings, BRT model (AUC = 0.894) had better efficiency than MARS model) AUC = 0.841) for gully erosion modeling and located in very good class of accuracy. In addition, altitude, aspect, slope degree, and land use were selected as the most conditioning agents on the gully erosion occurrence. The results of this research can be used for the prioritization of critical areas and better decision making in the soil and water management in the Valasht Watershed. In addition, the used models are recommended for spatial modeling in other regions of the worlds.

Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils

Abstract: Research highlights? The use of multiple regression (MR), artificial neural network (ANN) and artificial neuro-fuzzy inference system (ANFIS) models, for the prediction of swell percent of soils, was described and compared. ? However the accuracies of ANN and ANFIS models may be evaluated relatively similar, it is shown that the constructed ANN models of RBF and MLP exhibit a high performance than ANFIS and multiple regression for predicting swell percent of clays. ? The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. ? The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations. In the recent years, new techniques such as; artificial neural networks and fuzzy inference systems were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. Determination of swell potential of soil is difficult, expensive, time consuming and involves destructive tests. In this paper, use of MLP and RBF functions of ANN (artificial neural networks), ANFIS (adaptive neuro-fuzzy inference system) for prediction of S% (swell percent) of soil was described, and compared with the traditional statistical model of MR (multiple regression). However the accuracies of ANN and ANFIS models may be evaluated relatively similar. It was found that the constructed RBF exhibited a high performance than MLP, ANFIS and MR for predicting S%. The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations.

Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia

Abstract: The current study aimed at evaluating the capabilities of seven advanced machine learning techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF), Multivariate Adaptive Regression Spline (MARS), Artificial Neural Network (ANN), Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), and Naive Bayes (NB), for landslide susceptibility modeling and comparison of their performances. Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue. This study was carried out using GIS and R open source software at Abha Basin, Asir Region, Saudi Arabia. First, a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources. All the landslide areas were randomly separated into two groups with a ratio of 70% for training and 30% for validating purposes. Twelve landslide-variables were generated for landslide susceptibility modeling, which include altitude, lithology, distance to faults, normalized difference vegetation index (NDVI), landuse/landcover (LULC), distance to roads, slope angle, distance to streams, profile curvature, plan curvature, slope length (LS), and slope-aspect. The area under curve (AUC-ROC) approach has been applied to evaluate, validate, and compare the MLTs performance. The results indicated that AUC values for seven MLTs range from 89.0% for QDA to 95.1% for RF. Our findings showed that the RF (AUC ​= ​95.1%) and LDA (AUC ​= ​941.7%) have produced the best performances in comparison to other MLTs. The outcome of this study and the landslide susceptibility maps would be useful for environmental protection.