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.     

Application of Tree-Based Predictive Models to Forecast Air Overpressure Induced by Mine Blasting

Natural Resources Research - In surface mines and underground excavations, every blasting operation can have some destructive environmental impacts, among which air overpressure (AOp) is of major...     

Sustainable multivariate analysis for land use management in El-Sharkiya,Egypt

In Egypt, major sustainability variables could be identified as scarce of soil and water resources, environmental degradation, rapid population growth, institutional arrangement that includes land tenure and farm fragmentation, agricultural administration, lack of infrastructure, and credit utilization. The main objective of the current work is to evaluate the sustainable land use management (SLM) model through biophysics and socioeconomic elements for the purpose of combating sustainability constraints that preclude the agricultural development geospatially. In this research, from the geomorphologic point of view, the obtained results showed three main landscapes. They were identified in the study area as: fluviolacustrine plain, Aeolian deposits, and flood plain. The study area was dominated by some physical and chemical degradation processes with different scales breaking down the equilibrium of soil stability. The SLM model was implemented and assessed from multivariate perspective points of productivity, security, protection, economic viability, and social acceptability. Four SLM classes were outlined as follows: class I, land management practices that did meet sustainability requirements with a score ≥0.65, which represented 31.0 % of the considered agricultural study area; class II, land management practices that were marginally above the sustainability threshold and represented 12.6 %; class III, land management practices that were slightly below the threshold of sustainability and represented 8.60 %; class IV, land management practices that did not meet sustainability requirements with index values >0.1 that represented 47.86 %. As a general conclusion, it is found that land management practices tend to be unsustainable in the area under investigation for certain constraints that play motivated roles in lowering the targeted land sustainability.     

Strong ground motion simulation during the November 1759 Earthquake along Serghaya Fault in the metropolitan of Damascus, Syria

The seismic hazard potential for metropolitan of Damascus, Syria is mainly controlled by earthquakes along Serghaya Fault which is a branch of Dead Sea Fault System. In this study, strong ground motion due to the November 1759 Earthquake along the fault of Serghaya was estimated with a numerical simulation technique. In the simulation, the Kostrov-like slip-velocity function was used as an input to the discrete wave number method to simulate the strong ground motions in a broadband frequency range. In order to model the incoherent rupture propagation which can excite large high-frequency waves, random numbers are added to arrival time of circular rupture front. MMI intensities calculated from the synthetic ground motions are compared with the observed values by Ambraseys and Barazangi (J Geophys Res 94:4007-4013, 1989). The calculated intensities are in good agreement with the observed ones at the most sites that validate appropriateness of the proposed source model. The PGA and PGV in the eastern region of Damascus city are higher than those in the western region due to the effects of local site amplification. The simulated high-frequency (1.0–6.0 Hz) ground motions for the sites in the Damascus city are higher than the design requirements defined by the Syrian building code. Furthermore, the simulated high-frequency ground motions for sites in the focal region are bigger than the design requirements in the case of the near-fault factors and are not considered. That demonstrates the appropriateness of considering the near-fault factors for a site near the focal region as introduced by the new building code.     

Estimating the rainfall erosivity for management planning in the Eastern Desert,Egypt

Soil erosion is one of the major causes of land degradation in arid areas. Soil erosion models, e.g. the revised universal soil loss equation (RUSLE), use arithmetical expressions to explore relationships among various processes occurring in the terrain. The established model includes soil parameters, slope, climate and human activities to estimate the water erosion rate and sediment yield. In this study, an approach was adopted to integrate RUSLE model and geographic information system to detect erosion vulnerability and determine the soil erosion risk in the study area. The study area is situated in the Eastern Desert, Egypt. Ground truth data were examined to represent two regions: Luxor-Suhag and Suhag–ElMinya. These regions are exampled by four dry valleys named Sannor, Tarfa, Asyut and Qena, which are planned for agricultural development. The results indicate high risk of water erosion and sediment load discharge into the cultivated land in Luxor–Suhag region. The other region of Suhag–ElMinya is moderately affected by water and sediment load discharge. A higher soil erosion rate was found in Qena wadi followed by Asyut, then Tarfa and Sannur, respectively.