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The optimal operation of dam reservoirs can be programmed and managed by predicting the inflow to these structures more accurately. To this end, there are various linear and nonlinear models. However, some hydrological problems like inflow with extreme seasonal variation are not purely linear or nonlinear. To improve the forecasting accuracy of this phenomenon, a linear Seasonal Auto Regressive Integrated Moving Average (SARIMA) model is combined with a nonlinear Artificial Neural Network (ANN) model. This new model is used to predict the monthly inflow to the Jamishan dam reservoir in West Iran. A comparison of the SARIMA and ANN models with the proposed hybrid model's results is provided accordingly. More specifically, the models' performance in forecasting base and flood flows is evaluated. The effect of changing the forecasting period length on the models' accuracy is studied. The results of increasing the number of SARIMA model parameters up to five are investigated to achieve more accurate forecasting. The hybrid model predicts peak flood flows much better than the individual models, but SARIMA outperforms the other models in predicting base flow. The obtained results indicate that the hybrid model reduces the overall forecast error more than the ANN and SARIMA models. The coefficient of determination of the hybrid, ANN and SARIMA models were 0.72, 0.64 and 0.58, and the root mean squared error values were 1.02, 1.16 and 1.27 respectively, during the forecast period. Changing the forecasting length also indicated that these models can be used in the long term without increasing the forecast error. [ABSTRACT FROM AUTHOR]

Blasting is an important mining operation that usually produce several damaging consequences. Adverse rock fragmentation due to bench blasting is one of them. Hence, analysis of risk level and accurate estimation of particle size distribution of fragment size are of interest. This research developed a new model to simultaneously predict and risk assessment of rock fragmentation using 64 collected data from blasting operated in the Zarshouran gold mine in Iran. In this regard, a newly rock engineering system (RES) is developed based on the reliability information of Z-number theory and causal-effect relationship of the fuzzy cognitive map (FCM). This approach is named the reliability rock engineering causality system (RRECS). To do this, 15 principal effective parameters on rock fragment size were considered in the RRECS modeling process. The uncertainty of the interaction matrix was reduced using Z-number concept. Besides, the weight of effective parameters updated based on the combination of the nonlinear Hebbian algorithm (NLH) and differential evolution algorithm (DE) in FCM. The RRECS performance was validated by statistical linear and non-linear models. The results show R2, RSME, BIAS, and Accuracy using the proposed RRECS model calculated to be 0.957, 1.956, 0.001, and 96.741 for training and 0.931, 0.996, 0.016, and 99.053 for testing parts, respectively. Therefore, RRECS has performed better than the exponential, power, logarithmic, polynomial, and linear models. Furthermore, the sensitivity analysis results revealed that the hole diameter and powder factor parameters have the highest and lowest sensitivity on fragmentation, respectively. [ABSTRACT FROM AUTHOR]

Abstract: A novel hybrid method coupling genetic programming and orthogonal least squares, called GP/OLS, was employed to derive new ground-motion prediction equations (GMPEs). The principal ground-motion parameters formulated were peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD). The proposed GMPEs relate PGA, PGV and PGD to different seismic parameters including earthquake magnitude, earthquake source to site distance, average shear-wave velocity, and faulting mechanisms. The equations were established based on an extensive database of strong ground-motion recordings released by Pacific Earthquake Engineering Research Center (PEER). For more validity verification, the developed equations were employed to predict the ground-motion parameters of the Iranian plateau earthquakes. A sensitivity analysis was carried out to determine the contributions of the parameters affecting PGA, PGV and PGD. The sensitivity of the models to the variations of the influencing parameters was further evaluated through a parametric analysis. The obtained GMPEs are effectively capable of estimating the site ground-motion parameters. The equations provide a prediction performance better than or comparable with the attenuation relationships found in the literature. The derived GMPEs are remarkably simple and straightforward and can reliably be used for the pre-design purposes. [Copyright &y& Elsevier]

Abstract: Economic dispatch (ED) plays an important role in power system operation. ED problem is a non-smooth and non-convex problem when valve-point effects of generation units are taken into account. This paper presents an efficient hybrid evolutionary approach for solving the ED problem considering the valve-point effect. The proposed algorithm combines a fuzzy adaptive particle swarm optimization (FAPSO) algorithm with Nelder–Mead (NM) simplex search called FAPSO-NM. In the resulting hybrid algorithm, the NM algorithm is used as a local search algorithm around the global solution found by FAPSO at each iteration. Therefore, the proposed approach improves the performance of the FAPSO algorithm significantly. The algorithm is tested on two typical systems consisting of 13 and 40 thermal units whose incremental fuel cost functions take into account the valve-point loading effects. [Copyright &y& Elsevier]

Abstract: Physical properties of horticultural products are important in designing the sorting, transporting, processing and packaging systems. Among physical attributes, dimensions, weight, volume, projected areas and porosity are useful for designing grading systems. This study carried out on four superior walnut (Juglands regia) genotypes, which were selected from primary morphological screening of 608 genotypes in Neyriz city, Iran. G-570 with higher nut weight (12.46g), kernel weight (8.25g) and kernel ratio of 66.21% was a promising genotype among others. Regarding studied physical properties, G-570 was 44mm in diameter significantly different from other genotype. In this research walnut mass was predicted by using different physical properties with linear and nonlinear models as three classifications (dimension, projected areas and volume). Results showed that the highest correlation was between models number three, two and one. Among grading system based on dimensions in walnut (first classification), minor diameter model with nonlinear relation was the best and could be considered as a good model for economical and horticultural designing systems. [Copyright &y& Elsevier]

Abstract: Fruits with the similar weight and uniform shape are in high demand in terms of marketing value. Therefore, an awareness of grading fruit based on weight is crucial. A part of this research was aimed to present some physical properties of three Iranian apricot cultivars (Shams, Nakhjavan, and Jahangiri). In addition, apricot mass was predicted by different physical characteristics with linear and nonlinear models as three different classifications: (1), single or multiple variable regressions of apricot dimensional characteristics. (2), single or multiple variable regressions of apricot projected areas and (3), estimating apricot mass based on its volume. All properties considered in the current study were found to be statistically significant at the 1% probability level. The highest and the lowest dimensional characteristics were found for Jahangiri and Nakhjavan cultivars, respectively. Sphericity values had significant difference among the tested cultivars. The latter property values were 0.971, 0.917, and 0.973 for Shams, Nakhjavan, and Jahangiri cultivars, respectively. Based on the results, the surface area of the Jahangiri cultivar was found to be 6458.35mm2, followed by the Shams and Nakhjavan cultivars, which had a mean of 6115.36, and 4395.25mm2, respectively. In this paper, the coefficients of static friction of the cultivars on three different surfaces are also reported. The results showed that mass modeling of apricot based on minor diameter and three projected areas are the most appropriate models in the first and the second classifications, respectively. In third classification, the best model was obtained on the basis of the actual volume as M =0.997V m +0301, R 2 =0.98, R.S.E.=1.711 with R 2 =0.98. It was concluded that the suitable grading system of apricot mass is based on minor diameter as nonlinear relation: M =0.0019c 2.693, R 2 =0.96, R.S.E.=2.2. [Copyright &y& Elsevier]