80 research outputs found
Mapping of Groundwater Spring Potential in Karst Aquifer System Using Novel Ensemble Bivariate and Multivariate Models
No full textGroundwater 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)</p
Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping
No full textIn this study, hybrid integration of MultiBoosting based on two artificial intelligence methods (the radial basis function network (RBFN) and credal decision tree (CDT) models) and geographic information systems (GIS) were used to establish landslide susceptibility maps, which were used to evaluate landslide susceptibility in Nanchuan County, China. First, the landslide inventory map was generated based on previous research results combined with GIS and aerial photos. Then, 298 landslides were identified, and the established dataset was divided into a training dataset (70%, 209 landslides) and a validation dataset (30%, 89 landslides) with ensured randomness, fairness, and symmetry of data segmentation. Sixteen landslide conditioning factors (altitude, profile curvature, plan curvature, slope aspect, slope angle, stream power index (SPI), topographical wetness index (TWI), sediment transport index (STI), distance to rivers, distance to roads, distance to faults, rainfall, NDVI, soil, land use, and lithology) were identified in the study area. Subsequently, the CDT, RBFN, and their ensembles with MultiBoosting (MCDT and MRBFN) were used in ArcGIS to generate the landslide susceptibility maps. The performances of the four landslide susceptibility maps were compared and verified based on the area under the curve (AUC). Finally, the verification results of the AUC evaluation show that the landslide susceptibility mapping generated by the MCDT model had the best performance
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Efficiency of artificial neural networks in determining scour depth at composite bridge piers
No full textA GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran
No full textRock fall susceptibility assessment along a mountainous road: an evaluation of bivariate statistic, analytical hierarchy process and frequency ratio
No full textGIS-based spatial prediction of landslide susceptibility using logistic regression model
No full textExperimental Evaluation of the Fatigue Performance and Self-Healing Behavior of Nanomodified Porous Asphalt Mixtures Containing RAP Materials under the Aging Condition and Freeze-Thaw Cycle
No full textFirst, porous asphalt (PA) pavement possesses a lower strength and lifetime compared to typical dense-grade asphalt mixtures due to its large empty space structure. Second, PA pavements' fatigue life and durability are affected significantly by climate factors; the two most critical factors being aging conditions and moisture actions. Third, because of the environmental concerns connected with producing or repairing asphalt pavements using only virgin materials, studies have recommended reusing reclaimed asphalt pavement (RAP) materials. On the other hand, their use in road pavement is negative to the fatigue performance of asphalt pavements, especially PA. Therefore, modifying PA mixtures containing RAP to address the mentioned issues is necessary. Researchers have found that modifying asphalt mixes using nanotechnology is one of the more effective methods. The four-point bending beam fatigue test is one of the most dependable tests to assess the fatigue performance of asphalt mixtures, and evaluating the fatigue resistance of nano-modified PA mixes containing RAP under laboratory conditions by performing this test is essential. This study aims to investigate the fatigue behavior of different compounds of PA mixtures modified with nano zinc oxide (NZ) (0%, 2%, 4%, 6%, and 8%) containing various contents of RAP materials (0%, 25%, and 50%) under normal, long-term aging, and freeze-thaw (F-T) cycle conditions. Moreover, the self-healing capability of these PA samples was evaluated using this test by performing two 24-h recovery periods following the first and second loading. It can be inferred from the result that although adding RAP and inducting long-term aging and moisture-damaged conditions negatively influenced PA mixes' fatigue lives, incorporating NZ caused increases in these values by averages of 114%. Besides, results indicated that applied rest periods were observed to significantly impact PA specimens' self-healing capability, resulting in longer fatigue life for them. On average, conventional and NZ-modified PA mixes with/without RAP could recover up to 32 and 48% of their fatigue resistance in all conditions.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Pavement Engineerin
A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China
No full textNovel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping
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