Prediction of longitudinal dispersion coefficient in natural rivers using a cluster-based Bayesian network

The longitudinal dispersion coefficient is a key element in determining the distribution and transmission of pollution, especially when cross-sectional mixing is completed. However, the existing predictive techniques for this purpose exhibit great amounts of uncertainty. The main objective of this study is to present a more accurate model for predicting longitudinal dispersion coefficient in natural rivers and streams. Bayesian network (BN) approach was considered in the modeling procedure. Two forms of input variables including dimensional and dimensionless parameters were examined to find the best model structure. In order to increase the performance of the model, the clustering method as a preprocessing data technique was applied to categorize the data in separate groups with similar characteristics. An expansive data set consisting of 149 field measurements was used for training and testing steps of the developed models. Three performance evaluation criteria were adopted for comparison of the results of the different models. Comparison of the present results with the artificial neural network (ANN) model and also well-known existing equations showed the efficiency of the present model. The performance of dimensionless BN model 30% is more than dimensional ones in terms of the root mean square error. The accuracy criterion was increased from 70 to 83% by performing clustering analysis on the BN model. The BN-cluster model 43% is more accurate than ANN model in terms of the accuracy criterion. The results indicate that the BN-cluster model give 16% better results than the best available considered model in terms of the accuracy criterion. The developed model provides a suitable approach for predicting pollutant transport in natural rivers.     

Applying different scenarios for landslide spatial modeling using computational intelligence methods

Landslides every year impose extensive damages to human beings in various parts of the world; therefore, identifying prone areas to landslides for preventive measures is essential. The main purpose of this research is applying different scenarios for landslide susceptibility mapping by means of combination of bivariate statistical (frequency ratio) and computational intelligence methods (random forest and support vector machine) in landslide polygon and point formats. For this purpose, in the first step, a total of 294 landslide locations were determined from various sources such as aerial photographs, satellite images, and field surveys. Landslide inventory was randomly split into a testing dataset 70% (206 landslide locations) for training the different scenarios, and the remaining 30% (88 landslides locations) was used for validation purposes. To providing landslide susceptibility maps, 13 conditioning factors including altitude, slope angle, plan curvature, slope aspect, topographic wetness index, lithology, land use/land cover, distance from rivers, drainage density, distance from fault, distance from roads, convergence index, and annual rainfall are used. Tolerance and the variance inflation factor indices were used for considering multi-collinearity of conditioning factors. Results indicated that the smallest tolerance and highest variance inflation factor were 0.31 and 3.20, respectively. Subsequently, spatial relationship between classes of each landslide conditioning factor and landslides was obtained by frequency ratio (FR) model. Also, importance of the mentioned factors was obtained by random forest (RF) as a machine learning technique. The results showed that according to mean decrease accuracy, factors of altitude, aspect, drainage density, and distance from rivers had the greatest effect on the occurrence of landslide in the study area. Finally, the landslide susceptibility maps were produced by ten scenarios according to different ensembles. The receiver operating characteristics, including the area under the curve (AUC), were used to assess the accuracy of the models. Results of validation of scenarios showed that AUC was varying from 0.668 to 0.749. Also, FR and seed cell area index indicators show a high correlation between the susceptibility classes with the landslide pixels and field observations in all scenarios except scenarios 10_RF and 10_SVM. The results of this study can be used for landslides management and mitigation and development activities such as construction of settlements and infrastructure in the future.     

Petrography,provenance, diaganesis and depositional environment of Murree Formation in Jhelum Valley,Sub Himalayas,Azad Jammu and Kashmir,Pakistan

The clastic sediments of the Murree Formation of Miocene age are exposed in Jhelum valley areas of Azad Jammu and Kashmir Pakistan. Field observations revealed the cyclic deposition in the Murree Formation. The sandstone, siltstone, and shale constitute a single cycle within the formation. This single unit is divided into five different lithofacies which constitute the Bouma sequence in the Murree Formation. The Murree Formation shows faulted contacts with Panjal Formation and Nagri Formation in the study area. The modal mineralogy data obtained from the petrography of sandstone indicates that sandstone is litharenite and lithic greywacke. The mineralogical and textural data suggests that sandstone is compositionally mature and poorly to moderately sorted. The dominantly angular to sub angular quartz grains show nearness of the source area. Fractured and sutured quartz grain reveals tectonodiagentic changes that occurred in Murree Formation. The sandstone experienced diagenetic changes. The pressure solution and cementation reduced the primary porosity of sandstone. However, alteration of feldspar and fractures in grains have produced secondary porosity. The X-ray diffraction (XRD) of the shale samples indicates that shale of the Murree Formation is argillaceous and dominated by illite clay mineral. The illite crystallinity values indicate very low grade metamorphism of Murree Formation in core of Hazara Kashmir Syntaxis. The petrographic data suggests that the provenance of sandstone is recycled orogen. Quartz is of igneous and metamorphic origin. Feldspar (albite and microcline) composition suggests its derivation from acidic igneous rocks. The rock fragments of volcanics, slate, phyllite, and schist suggest igneous and metamorphic provenance. The petrographic data suggests that at the time of deposition of Murree Formation, igneous and low grade metamorphic rocks were exposed. However, presence of some clasts of carbonates indicates that sedimentary rocks were also exposed in the source region. The quartz content and clay minerals in the shale revealed that source region was igneous and metamorphic rocks. Cyclic deposition, lithofacies, and various sedimentary structures like cross bedding, ripple marks, and calcite concretions suggest that deposition of Murree Formation occurred in fluviatile environment by meandering river system having decreasing turbidity current.     

Effect of silica fume on compressive strength of oil-polluted concrete in different marine environments

In the present research, effect of silica fume as an additive and oil polluted sands as aggregates on compressive strength of concrete were investigated experimentally. The amount of oil in the designed mixtures was assumed to be constant and equal to 2% of the sand weight. Silica fume accounting for 10%, 15% and 20% of the weight is added to the designed mixture. After preparation and curing, concrete specimens were placed into the three different conditions: fresh, brackish and saltwater environments (submerged in fresh water, alternation of exposed in air & submerged in sea water and submerged in sea water). The result of compressive strength tests shows that the compressive strength of the specimens consisting of silica fume increases significantly in comparison with the control specimens in all three environments. The compressive strength of the concrete with 15% silica fume content was about 30% to 50% higher than that of control specimens in all tested environments under the condition of using polluted aggregates in the designed mixture.     

Full-scale experimental investigations on the response of a flooded passenger vehicle under subcritical conditions

Natural Hazards - Vehicles can be easily swept away by floodwaters once the flow velocity and depth reach certain critical limits, with probabilities toward fatality reported to be nearly 50%....