Active and online prediction of BOD5 in river systems using reduced-order support vector machine

Due to the limitations of hardware sensors for online measurement of the water quality parameters such as 5-day biochemical oxygen demand (BOD₅), the recent research efforts have focused on the software sensors for the rapid prediction of such parameters. The main objective in this research is to develop a reduced-order support vector machine (ROSVM) model based on the proper orthogonal decomposition to solve the time-consuming problem of the BOD₅ measurements. The performance of the newly developed methodology is tested on the Sefidrood River Basin, Iran. Subsequently, the predicted values of BOD₅, resulted from the selected developed ROSVM model, are compared with the results of support vector machine (SVM) model. According to the obtained results, selected ROSVM model seems to be more accurate, showing Person correlation coefficient (R) and root mean square error (RMSE) equal to 0.97 and 6.94, respectively. Further, the investigations based on developed discrepancy ratio (DDR) statistic for selection of the optimum model between the best accurate ROSVM and SVM models are carried out. Results of DDR statistic indicated superior performance of the selected ROSVM model comparing to the SVM technique for online prediction of BOD₅ in the Sefidrood River.     

Practical finite analytic methods for simulation of advection-dominated solute transport

Abstract

The practical finite-analytic (PFA) method was applied to the solution of the one-dimensional advection–dispersion equation (ADE) for solute transport in porous media under advection-dominated (high Peclet number, Pe) conditions. Several PFA spatial-temporal computational molecules were developed for Cauchy and pulse loading boundary conditions. The PFA solutions were compared with solutions from the upwind method and quadratic upwind differencing (QUICK) scheme. For all boundary conditions the trapezoidal explicit PFA (EPFA) computational molecule gave the most accurate results at very high Pe number as long as the Courant number (Cr) was close to one. Stability analysis shows that the PFA molecules are always stable for high Pe number.     

A Combination of Expert-Based System and Advanced Decision-Tree Algorithms to Predict Air-Overpressure Resulting from Quarry Blasting

Natural Resources Research - This study combined a fuzzy Delphi method (FDM) and two advanced decision-tree algorithms to predict air-overpressure (AOp) caused by mine blasting. The FDM was used...     

Health risk assessment of nitrate exposure in groundwater of rural areas of Gonabad and Bajestan,Iran

Prolonged exposure to excessive levels of nitrate through drinking water is a potential risk for human health. The current research reports the analytical results and associated health risk for water quality in term of nitrate in 39 groundwater samples during January 2018 in rural areas of Gonabad and Bajestan, Iran. Nitrate concentrations ranged from 1.8 to 82.2 and from 5.5 to 84.3 mg/L for Gonabad and Bajestan, respectively. In this work, the potential risk to human health was determined using the hazard quotient (HQ) for three age groups including adults, children and infants. Comparison of HQs among the 39 sampling sites showed that the rural areas in Bajestan had higher HQs than Gonabad. Among the studied groups, infants exposed to a higher risk than children and adults. The results also indicated that the health of individuals from nitrate exposure in most of the groundwater studied was not acceptable and most of the consumers were in danger from current nitrate concentrations. Therefore, there is an urgent need for enforcing effective plans to improve groundwater quality and to better manage and control probable nitrate contaminated sources.     

Urmia lake water level fluctuation hydro informatics modeling using support vector machine and conjunction of wavelet and neural network

In this research, the simulation of Urmia Lake water level fluctuation by means of two models was applied. For this, Support Vector Machines (SVM), and Neural Wavelet Network (NWN) models that conjugated both the wavelet function and ANN, developed for simulating the Urmia Lake water level fluctuation. The yearly data of rainfall, temperature and discharge to the Urmia Lake and water level fluctuation were used. Urmia Lake is the biggest and the hyper saline lake in Iran. The outcome of the SVM based models are compared with the NWN. The results of SVM model performs better than NWN and offered a practical solution to the problem of water level fluctuation predictions. Analysis results showed that the optimal situation occurred with use of precipitation, temperature and discharge for all station and water level fluctuations at the lag time of one year (RMSEs) of 0.23, 0.41 m obtained by SVM, NWN, respectively, and SSEs of 0.43, 1.33 and R ² of 0.97, 0 obtained by SVM, NWN, respectively. The results of SVM model show better accuracy in comparison with the NWN model.     

NMR Parameters Determination through ACE Committee Machine with Genetic Implanted Fuzzy Logic and Genetic Implanted Neural Network

Free fluid porosity and rock permeability, undoubtedly the most critical parameters of hydrocarbon reservoir, could be obtained by processing of nuclear magnetic resonance (NMR) log. Despite conventional well logs (CWLs), NMR logging is very expensive and time-consuming. Therefore, idea of synthesizing NMR log from CWLs would be of a great appeal among reservoir engineers. For this purpose, three optimization strategies are followed. Firstly, artificial neural network (ANN) is optimized by virtue of hybrid genetic algorithm-pattern search (GA-PS) technique, then fuzzy logic (FL) is optimized by means of GA-PS, and eventually an alternative condition expectation (ACE) model is constructed using the concept of committee machine to combine outputs of optimized and non-optimized FL and ANN models. Results indicated that optimization of traditional ANN and FL model using GA-PS technique significantly enhances their performances. Furthermore, the ACE committee of aforementioned models produces more accurate and reliable results compared with a singular model performing alone.     

Shallow/upper crustal shear wave structure of the Tehran region (Central Alborz,Iran) from the inversion of Rayleigh wave dispersion measurements

Surface wave dispersion curves from microearthquakes are used to obtain group velocity dispersion maps. The calculation of the local dispersion curves for each grid point from these maps then produces the input data to retrieve the 3D shear wave velocity model of the Tehran region. The group velocity maps indicate that the tomographic results agree well with the three main tectonic features and the geological units in the study area. The tomographic maps generally possess high-velocity structures across most of the mountain belts (Central Alborz and east-southeast mountains), whereas the Tehran Basin correlates to a low-velocity structure. Increasing the period in the study area highlights four independent low-velocity zones that reflect faults and fault junction systems. The shear wave velocity profiles indicate that the depth to bedrock exhibits southward variation ranging from ~?300 m to ~?1500 m. We also focus our analysis on the existence of faults within the shear wave profiles and discuss the low shear wave velocity anomalies deeper than 2 km result from the main fault structures (e.g., North Tehran, North-South Rey and Parchin). Furthermore, we argue that the dip angle of the North Tehran fault varies along fault strike, whereas the North-South Rey fault possesses a constant dip angle. Moreover, initial model uncertainties and checkerboard resolution tests are used to identify reliable and robust anomaly features in the 3D shear wave velocity model and 2D tomographic maps, respectively. Microearthquake analysis provides an effective approach for studying the upper crustal structure heterogeneity, especially the fault structure, of the Tehran region.     

Prediction of River Stage-Discharge Process Based on a Conceptual Model Using EEMD-WT-LSSVM Approach

Water Resources - This study proposed a hybrid pre-processing approach along with a conceptual model to enhance the accuracy of river discharge prediction. In order to achieve this goal, Ensemble...     

Artificial Neural Network and Firefly Algorithm for Estimation and Minimization of Ground Vibration Induced by Blasting in a Mine

Natural Resources Research - It is of a high importance to introduce intelligent systems for estimation and optimization of blasting-induced ground vibration because it is one the most unwanted...