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Landslides are a dangerous natural hazard that can critically harm road infrastructure in mountainous places, resulting in significant damage and fatalities. The primary purpose of this study was to assess the efficacy of three machine learning algorithms (MLAs) for landslide susceptibility mapping including random forest (RF), decision tree (DT), and support vector machine (SVM). We selected a case study region that is frequently affected by landslides, the important Kamyaran–Sarvabad road in the Kurdistan province of Iran. Altogether, 14 landslide evaluation factors were input into the MLAs including slope, aspect, elevation, river density, distance to river, distance to fault, fault density, distance to road, road density, land use, slope curvature, lithology, stream power index (SPI), and topographic wetness index (TWI). We identified 64 locations of landslides by field survey of which 70% were randomly employed for building and training the three MLAs while the remaining locations were used for validation. The area under the receiver operating characteristics (AUC) reached a value of 0.94 for the decision tree compared to 0.82 for the random forest, and 0.75 for support vector machines model. Thus, the decision tree model was most accurate in identifying the areas at risk for future landslides. The obtained results may inform geoscientists and those in decision-making roles for landslide management. Validerad;2023;Nivå 2;2023-06-19 (hanlid);Funder: University of Kurdistan, Iran (02-9-3786, 01-9-22595)

Landslides can be a major challenge in mountainous areas that are influenced by climate and landscape changes. In this study, we propose a hybrid machine learning model based on a rotation forest (RoF) meta classifier and a random forest (RF) decision tree classifier called RoFRF for landslide prediction in a mountainous area near Kamyaran city, Kurdistan Province, Iran. We used 118 landslide locations and 25 conditioning factors from which their predictive usefulness was measured using the chi-square technique in a 10-fold cross-validation analysis. We used the sensitivity, specificity, accuracy, F1-measure, Kappa, and area under the receiver operating characteristic curve (AUC) to validate the performance of the proposed model compared to the Artificial Neural Network (ANN), Logistic Model Tree (LMT), Best First Tree (BFT), and RF models. The validation results demonstrated that the landslide susceptibility map produced by the hybrid model had the highest goodness-of-fit (AUC = 0.953) and higher prediction accuracy (AUC = 0.919) compared to the benchmark models. The hybrid RoFRF model proposed in this study can be used as a robust predictive model for landslide susceptibility mapping in the mountainous regions around the world. Validerad;2022;Nivå 2;2022-06-15 (joosat);Funder: University of Kurdistan, Iran (00-9-34027-4469)

We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm (GA). A total of 118 landslide locations were recorded and divided in the training and testing datasets. We selected 25 conditioning factors, and of these, we specified the most important ones by an information gain ratio (IGR) technique. We assessed the performance of the DP model using statistical measures including sensitivity, specificity, accuracy, F1-measure, and area under-the-receiver operating characteristic curve (AUC). Three benchmark algorithms, i.e., support vector machine (SVM), REPTree, and NBTree, were used to check the applicability of the proposed model. The results by IGR concluded that of the 25 conditioning factors, only 16 factors were important for our modeling procedure, and of these, distance to road, road density, lithology and land use were the four most significant factors. Results based on the testing dataset revealed that the DP model had the highest accuracy (0.926) of the compared algorithms, followed by NBTree (0.917), REPTree (0.903), and SVM (0.894). The landslide susceptibility maps prepared from the DP model with AUC = 0.870 performed the best. We consider the DP model a suitable tool for landslide susceptibility mapping. Validerad;2022;Nivå 2;2022-02-18 (sofila);Funder: University of Kurdistan, Iran (grant no. 11-99-4469)

This paper aims to apply and compare the performance of the three machine learning algorithms&ndash, support vector machine (SVM), bayesian logistic regression (BLR), and alternating decision tree (ADTree)&ndash, to map landslide susceptibility along the mountainous road of the Salavat Abad saddle, Kurdistan province, Iran. We identified 66 shallow landslide locations, based on field surveys, by recording the locations of the landslides by a global position System (GPS), Google Earth imagery and black-and-white aerial photographs (scale 1: 20,000) and 19 landslide conditioning factors, then tested these factors using the information gain ratio (IGR) technique. We checked the validity of the models using statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC). We found that, although all three machine learning algorithms yielded excellent performance, the SVM algorithm (AUC = 0.984) slightly outperformed the BLR (AUC = 0.980), and ADTree (AUC = 0.977) algorithms. We observed that not only all three algorithms are useful and effective tools for identifying shallow landslide-prone areas but also the BLR algorithm can be used such as the SVM algorithm as a soft computing benchmark algorithm to check the performance of the models in future.

The declining water level in Lake Urmia has become a significant issue for Iranian policy and decision makers. This lake has been experiencing an abrupt decrease in water level and is at real risk of becoming a complete saline land. Because of its position, assessment of changes in the Lake Urmia is essential. This study aims to evaluate changes in the water level of Lake Urmia using the space-borne remote sensing and GIS techniques. Therefore, multispectral Landsat 7 ETM+ images for the years 2000, 2010, and 2017 were acquired. In addition, precipitation and temperature data for 31 years between 1986 and 2017 were collected for further analysis. Results indicate that the increased temperature (by 19%), decreased rainfall of about 62%, and excessive damming in the Urmia Basin along with mismanagement of water resources are the key factors in the declining water level of Lake Urmia. Furthermore, the current research predicts the potential environmental crisis as the result of the lake shrinking and suggests a few possible alternatives. The insights provided by this study can be beneficial for environmentalists and related organizations working on this and similar topics. Validerad;2020;Nivå 2;2020-06-26 (alebob)

Groundwater is an important natural resource in arid and semi-arid environments, where discharge from karst springs is utilized as the principal water supply for human use. The occurrence of karst springs over large areas is often poorly documented, and interpolation strategies are often utilized to map the distribution and discharge potential of springs. This study develops a novel method to delineate karst spring zones on the basis of various hydrogeological factors. A case study of the Bojnourd Region, Iran, where spring discharge measurements are available for 359 sites, is used to demonstrate application of the new approach. Spatial mapping is achieved using ensemble modelling, which is based on certainty factors (CF) and logistic regression (LR). Maps of the CF and LR components of groundwater potential were generated individually, and then, combined to prepare an ensemble map of the study area. The accuracy (A) of the ensemble map was then assessed using area under the receiver operating characteristic curve. Results of this analysis show that LR (A = 78%) outperformed CF (A = 67%) in terms of the comparison between model predictions and known occurrences of karst springs (i.e., calibration data). However, combining the CF and LR results through ensemble modelling produced superior accuracy (A = 85%) in terms of spring potential mapping. By combining CF and LR statistical models through ensemble modelling, weaknesses in CF and LR methods are offset, and therefore, we recommend this ensemble approach for similar karst mapping projects. The methodology developed here offers an efficient method for assessing spring discharge and karst spring potentials over regional scales. Validerad;2020;Nivå 2;2020-03-31 (johcin)

Zrebar Lake is one of the largest freshwater lakes in Iran and it plays an important role in the ecosystem of the environment, while its desiccation has a negative impact on the surrounded ecosystem. Despite this, this lake provides an interesting recreation setting in terms of ecotourism. The prediction and forecasting of the water level of the lake through simple but practical methods can provide a reliable tool for future lake water resource management. In the present study, we predict the daily water level of Zrebar Lake in Iran through well-known decision tree-based algorithms, including the M5 pruned (M5P), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). We used five different water input combinations to find the most effective one. For our modeling, we chose 70% of the dataset for training (from 2011 to 2015) and 30% for model evaluation (from 2015 to 2017). We evaluated the models&rsquo, performances using different quantitative (root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), percent bias (PBIAS) and ratio of the root mean square error to the standard deviation of measured data (RSR)) and visual frameworks (Taylor diagram and box plot). Our results showed that water level with a one-day lag time had the highest effect on the result and, by increasing the lag time, its effect on the result was decreased. This result indicated that all the developed models had a good prediction capability, but the M5P model outperformed the others, followed by RF and RT equally and then REPT. Our results showed that these algorithms can predict water level accurately only with a one-day lag time in water level as an input and they are cost-effective tools for future predictions.

Local scour depth at complex piers (LSCP) cause expensive costs when constructing bridges. In this study, a hybrid artificial intelligence approach of random subspace (RS) meta classifier, based on the reduced error pruning tree (REPTree) base classifier, namely RS-REPTree, was proposed to predict the LSCP. A total of 122 laboratory datasets were used and portioned into training (70%: 85 cases) and validation (30%: 37 cases) datasets for modeling and validation processes, respectively. The statistical metrics such as mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (R), and Taylor diagram were used to check the goodness-of-fit and performance of the proposed model. The capability of this model was assessed and compared with four state-of-the-art soft-computing benchmark algorithms, including artificial neural network (ANN), support vector machine (SVM), M5P, and REPTree, along with two empirical models, including the Florida Department of Transportation (FDOT) and Hydraulic Engineering Circular No. 18 (HEC-18). The findings showed that machine learning algorithms had the highest goodness-of-fit and prediction accuracy (0.885 < R < 0.945) in comparison to the other models. The results of sensitivity analysis by the proposed model indicated that pile cap location (Y) was a more sensitive factor for LSCP among other factors. The result also depicted that the RS-REPTree ensemble model (R = 0.945) could well enhance the prediction power of the REPTree base classifier (R = 0.885). Therefore, the proposed model can be useful as a promising technique to predict the LSCP. Validerad;2020;Nivå 2;2020-02-04 (johcin)

Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas. Validerad;2020;Nivå 2;2020-01-24 (johcin)