Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.

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.

Physico-chemical properties of diethyl ether/linseed oil (DEE/LO) fuel blends were empirically tested in this article for the first time. In particular, kinematic viscosity (ν), density (ρ), lower heating value (LHV), cold filter plugging point (CFPP) and surface tension (σ) were examined. For this research diethyl ether (DEE) was blended with linseed oil (LO) in volumetric ratios of 10%, 20% and 30%. Obtained results were compared with literature data of diethyl ether/rapeseed oil (DEE/RO) fuel blends get in previous research in such a way looking on differences also between oil types. It was found that DEE impacts significantly on the reduction of plant oil viscosity, density and surface tension and improve low temperature properties of tested oils. In particular, the addition of 10% DEE to LO effectively reduces its kinematic viscosity by 53% and even by 82% for the blend containing 30% DEE. Tested ether reduces density and surface tension of LO up to 6% and 25% respectively for the blends containing 30% DEE. The measurements of the CFPP showed that DEE significantly improves the low temperature properties of LO. In the case of the blend containing 30% DEE the CFPP can be lowered up to −24 °C. For this reason DEE/LO blends seem to be valuable as a fuel for diesel engines in the coldest season of the year. Moreover, DEE/LO blends have been tested in the engine research. Based on results it can be stated that the engine operated with LO results in worse performance compared with regular diesel fuel (DF). However, it was found that these disadvantages could be reduced with DEE as a component of the fuel mixture. Addition of this ether to LO improves the quality of obtained fuel blends. For this reason, the efficiency of DEE/LO blend combustion process is similar for the engine fuelled with regular diesel fuel. In this research it was confirmed that the smoke opacity reaches the highest value for the engine fuelled with plant oils. However, addition of 20% DEE reduces this emission to the value comparable for the engine operated with diesel fuel.