Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction

Artificial neural networks (ANNs) are suitable for modeling solid waste generation. In the present study, four training functions, including resilient backpropagation (RP), scale conjugate gradient (SCG), one step secant (OSS), and Levenberg–Marquardt (LM) algorithms have been used. The main goal of this research is to develop an ANN model with a simple structure and ample accuracy. In the first step, an appropriate ANN model with 13 input variables is developed using the afore-mentioned algorithms to optimize the network parameters for weekly solid waste prediction in Mashhad, Iran. Subsequently, principal component analysis (PCA) and Gamma test (GT) techniques are used to reduce the number of input variables. Finally, comparison amongst the operation of ANN, PCA-ANN, and GT-ANN models is made. Findings indicated that the PCA-ANN and GT-ANN models have more effective results than the ANN model. These two models decrease the number of input variables from 13 to 7 and 5, respectively.     

The optimal dam site selection using a group decision-making method through fuzzy TOPSIS model

The complex and controversial task of selecting a dam site in a river basin can be successfully achieved using science-informed multi-criteria decision-making (MCDM) techniques. In this paper, we describe the application of the group fuzzy TOPSIS model for optimal ranking of the case study of Kandoleh dam sites in Kermanshah province, Iran, involving 18 input criteria. In this study, decision-making committee was made up of 20 involved decision makers. The comments of four non-biased, external experts in dam site selection were also used. The triangular fuzzy numbers were used to apply experts’ opinions on the selection criteria. In total, four alternative sites were assessed based on the technical, economic, social and environmental considerations and the data were analyzed using fuzzy TOPSIS MCDM model. Ranking results were compared with multi-criteria decision-making models, including the ELimination and Choice Expressing the REality and simple additive weighting. This logical, open and transparent framework provides a science-informed decision-making approach for complex problems such as optimal dam site selection. Finally, using sensitivity analysis, local studies and group discussions, we demonstrated the multiple benefits of the proposed novel method for a science-informed, open and transparent method for optimal ranking of the dam site candidates.     

Environmental protection stability of river bed and banks using convex, concave, and linear bed sills

River bed scourings are a major environmental problem for fish and aquatic habitat resources. In this study, to prevent river bed and banks from scouring, different types of bed sills including convex, concave and linear patterns were installed in a movable channel bed in a laboratory flume. The bed sills were tested with nine different arrangements and under different flow conditions. To find the most effective bed sill pattern, the scouring depth was measured downstream of the bed sill for a long experimental duration. The scour depth was measured at the middle and at the end of each experimental test for different ratios of the arch radius to the channel width $(r/\emph{w})$ . The experimental results indicated that the convex pattern with $r/\emph{w}=0.35$ produced minimum bed scouring depth at the center line whereas the concave pattern with $r/\emph{w}=0.23$ produced the minimum scour depth at the wall banks. Therefore, the convex pattern was the most effective configuration for prevention of scouring at the center line of the river while the concave pattern was very effective to prevent scouring at the river banks. These findings can be suggested to be used in practical applications.     

Flavonoids of 17 species of Euphorbia (Euphorbiaceae) in Iran

Euphorbia is one of the largest genus in Euphorbiaceae with more than 2000 species in the world and about 100 species in Iran. Flavonoids characters of 17 collected Euphorbia species from different parts of Markazi Province, Iran area were studied. Aqueous-ethanolic extracts of leaves were examined to practice flavonoid detection, isolation, and identification by 2-Dimensional Paper Chromatography (2-DPC), Thin Layer Chromatography (TLC), and available standard flavonoids. Voucher specimens of each sample were prepared for reference as herbarium vouchers. Studies of leaf flavonoids showed some phytochemical characters such as total number of flavonoids, kaempferol, quercetin, myricetin, flavone, and dihydroflavonol glycoside are valuable for chemotaxonomy and their usage.     

Chemometric Analysis of Surface Water Quality Data: Case Study of the Gorganrud River Basin, Iran

Canonical correlation analysis (CCA), principal component analysis (PCA), and principal factor analysis (PFA) have been adopted to provide ease of understanding: interpretation of a large complex data set in the Gorganrud River monitoring networks, evaluation of the temporal and spatial variations of water quality, and finally identification of monitoring stations and parameters which are most important in assessing annual variations of water quality in the river. In accomplishing the research, 11 surface water quality data related to both of physical and chemical parameters have been collected from seven monitoring stations from 1996 to 2002. In general, our results from CCA method indicated strong relationship between physical and chemical parameters in the Gorganrud River. In addition, analyzing data through the PCA and PFA techniques revealed that all monitoring stations are important in explaining the annual variation of data set. From the point of view of the degree of importance of parameters contributing to water quality variations, further investigations by running two scenarios (rotated factor correlation coefficient value equal to 0.95 and 0.90 for the first and second scenarios, respectively) showed that the important parameters in one season may not be important for another season. For example, unlike in summer, water temperature, total suspended solids, total phosphorous, and nitrate parameters were important, electrical conductivity, and turbidity parameters had been realized as important parameters in spring through the first scenario.     

Progress and challenges of helical-shaped geothermal heat exchangers

Environmental Science and Pollution Research - Among various types of renewable energy, geothermal energy is recognized as an effective method for supplying thermal energy. Ground heat exchangers,...     

Development of GP and GEP models to estimate an environmental issue induced by blasting operation

Air overpressure (AOp) is one of the most adverse effects induced by blasting in the surface mines and civil projects. So, proper evaluation and estimation of the AOp is important for minimizing the environmental problems resulting from blasting. The main aim of this study is to estimate AOp produced by blasting operation in Miduk copper mine, Iran, developing two artificial intelligence models, i.e., genetic programming (GP) and gene expression programming (GEP). Then, the accuracy of the GP and GEP models has been compared to multiple linear regression (MLR) and three empirical models. For this purpose, 92 blasting events were investigated, and subsequently, the AOp values were carefully measured. Moreover, in each operation, the values of maximum charge per delay and distance from blast points, as two effective parameters on the AOp, were measured. After predicting by the predictive models, their performance prediction was checked in terms of variance account for (VAF), coefficient of determination (CoD), and root mean square error (RMSE). Finally, it was found that the GEP with VAF of 94.12%, CoD of 0.941, and RMSE of 0.06 is a more precise model than other predictive models for the AOp prediction in the Miduk copper mine, and it can be introduced as a new powerful tool for estimating the AOp resulting from blasting.     

Assessment of metals in fourteen species of vegetables and crops cultivated in a suburban area using multivariate analyses

Concentrations of seven metals (Cd, Cr, Mn, Ni, Pb, V, and Zn) were determined in 14 plant species including vegetables and crops from an agricultural area located at the Southern part of the Iranian capital, near the Tehran oil refinery. Different statistical methods, especially multivariate ones, were employed for the analysis of the results. Based on the results of agglomerative hierarchical method for clustering of the grid cells, some similarities between the dendrograms of Pb, Ni, and V and, furthermore, between Mn and Cr were observed that could be attributed to their similar distribution sources in the study area. The principal component analysis showed that with respect to accumulation of all the metals in the plant species, two main groups could be identified. The highest mean concentrations of all metals (except for Pb) were found in onions. In all plants the mean concentrations of Cd and Pb were considerably higher than limits stated in international guidelines for human consumption. The levels of the metals in onions exceeded these limits. Moreover, the Cr concentrations in mint and basil and the Zn concentrations in tarragon and parsley were higher than the established limits.