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.

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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.

In this paper, selected physicochemical properties (such as lower heating value (LHV), kinematic viscosity, density, lubricity, cold filter plugging point (CFPP), and cetane number (CN), as well as miscibility of diethyl ether (DEE)/ diesel oil blends) were experimentally determined. DEE was added to diesel oil, meeting the requirements of European standard PN-EN 590, in volumetric proportions of 5, 10, 15, and 20 vol %. The LHV, kinematic viscosity, and density of the blends were lower than the values obtained for the tested diesel oil. Especially, it was found that DEE has significant influence on diesel oil viscosity. The addition of merely 5% DEE to diesel oil decreased its viscosity by 26%. It was shown that the lubricity of all tested blends is reduced, but not as significantly as viscosity. It was found that the temperature of the CFPP decreased when DEE was added to diesel oil. The results show that miscibility of all tested fuel blends is excellent in a wide range of temperature changes. The engine experiments were carried out on two different diesel engines. CN values of the tested blends were determined using a Waukesha Co. research engine, according to the procedure presented in European standard EN ISO 5165:2003. It was observed that the addition of DEE slightly increased the CN of the diesel oil. However, it did not have any influence on shortening of the ignition delay period in the tested AD3.152 diesel engine with direct fuel injection.

This paper addresses the issue of metrological accuracy of instantaneous in-cylinder pressure measurement in a diesel engine test bed. In studies, the central unit has been the single-cylinder AVL 5402 engine. The pressure measurement was performed with a sensor designed for thermodynamic analysis, and the results were related to the crank angle, where two rotations corresponding to the four-stroke working cycle were denoted as angles between −360° and +360°. The novelty of this paper is the proposition of how to perform a type A uncertainty estimation of the in-cylinder pressure measurement and to assess its repeatability. It was demonstrated that repeatability of the measurement during the ignition process was difficult to estimate because of the phenomena that cannot ensure the repeatability conditions. To solve the problem, two methods were proposed. In one method, the pressure was measured in the subsequent cycles immediately after the ignition was turned off, and in another method, the engine was driven by a starter. The latter method provided maximal pressure values much lower than during usual tests. The obtained repeatability of measured pressure was %EV = 0.4%, which proved high capability of the evaluated measurement system.