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In this study, the pollution risks related to arsenic, fluorine, iron, physicochemical parameters, and various indices have been investigated in the Bazoft dam site. The proposed dam is a double-curvature arch dam with a height of 211 m in order to produce hydroelectric energy and the supply of agricultural and drinking water in the west of Iran. Chemical analysis of water samples obtained from springs, river, and boreholes in the dam site was conducted. Anions such as HCO3, Cl, SO4, CO3, and NO3, and cations including Ca, Na, Mg, K, F, Fe, and As had the highest amounts, respectively. The concentration of assessed variables was lower than the WHO permissible limit. The results of the principal factor analysis (PCA) showed that four factors control 82% of the water quality variations. The factors such as EC, TDS, Na, K, SO4−, CO3, HCO3, total hardness, and alkalinity with 43.10% were the most important factors in the dam site. The heavy metals of iron and arsenic have no meaningful relationship with any of the ions. The accuracy of the relations was investigated based on different statistical criteria. The main processes in controlling the water quality are weathering and the dissolution of the carbonates caused by the interaction of water and rock. Ionic ratios indicated that the water origin of the springs is the dolomitic aquifers and the main source of magnesium is carbonate dissolution. The water resources of the dam site are corrosive, and all indices indicate the appropriate quality of water for irrigation and drinking purposes. Filed studies and boreholes water level variations showed that karstification is limit in the dam site. [ABSTRACT FROM AUTHOR]

Blasting is the predominant rock fragmentation technique in civil constructions, underground and surface mines. Flyrock is the unwanted throw of rock fragments during blasting and is the major cause of considerable damage in and around the mines. The present research aimed to propose a new intelligence-based method to predict flyrock. In this regard, the recurrent fuzzy neural network (RFNN) combined with the genetic algorithm (GA) is proposed. For checking the suitability of the RFNN-GA model, artificial neural network (ANN), hybrid ANN and GA and a nonlinear regression model were also employed. To achieve the aims of the research, data for 70 blasting sites including four input parameters (spacing, burden, stemming and maximum charge per delay) and one output parameter (flyrock) were gathered from two quarry mines at the Shur River dam, Iran. The performance of the proposed prediction methods was then assessed with statistical evaluation criteria, i.e., R-square and root mean square error. The results indicate the proposed RFNN-GA model was more superior for prediction of flyrock than the GA-ANN, ANN and nonlinear regression models. According to a sensitivity analysis, the maximum charge per delay was the most influential parameter in flyrock prediction in this case. [ABSTRACT FROM AUTHOR]

Regular optimization techniques have been widely used in landslide-related problems. This paper outlines two novel optimizations of artificial neural network (ANN) using grey wolf optimization (GWO) and biogeography-based optimization (BBO) metaheuristic algorithms in the Ardabil province, Iran. To this end, these algorithms are synthesized with a multi-layer perceptron (MLP) neural network for optimizing its computational parameters. The used spatial database consists of fourteen landslide conditioning factors, namely elevation, slope aspect, land use, plan curvature, profile curvature, soil type, distance to river, distance to road, distance to fault, rainfall, slope degree, stream power index (SPI), topographic wetness index (TWI) and lithology. 70% of the identified landslides are randomly selected to train the proposed models and the remaining 30% is used to evaluate the accuracy of them. Also, the frequency ratio theory is used to analyze the spatial interaction between the landslide and conditioning factors. Obtained values of area under the receiver operating characteristic curve, as well as mean square error and mean absolute error showed that both GWO and BBO hybrid algorithms could efficiently improve the learning capability of the MLP. Besides, the BBO-based ensemble surpasses other implemented models. [ABSTRACT FROM AUTHOR]

Forests are important dynamic systems which are widely affected by fire worldwide. Due to the complexity and non-linearity of the forest fire problem, employing hybrid evolutionary algorithms is a logical task to achieve a reliable approximation of this environmental threat. Three fuzzy-metaheuristic ensembles, based on adaptive neuro-fuzzy inference systems (ANFIS) incorporated with genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) evolutionary algorithms are used to produce the forest fire susceptibility map (FFSM) of a fire-prone region in Iran. A sensitivity analysis is also executed to evaluate the effectiveness of the proposed ensembles in terms of time and complexity. The results revealed that all models produce FFSMs with acceptable accuracy. However, the superiority of the GA-ANFIS was shown in both recognizing the pattern (AUROC train = 0.912 and Error = 0.1277) and predicting unseen fire events (AUROC test = 0.850 and Error = 0.1638). The optimized structures of the proposed GA-ANFIS and PSO-ANFIS ensembles could be good alternatives to traditional forest fire predictive models, and their FFSMs can be promisingly used for future planning and decision making in the proposed area. • Three Fuzzy-Metaheuristic Ensembles proposed for Spatial Forest Fire Susceptibility Assessment. • The superiority of the GA-ANFIS was proved. • Pattern recognition, as well as unseen fire event predictions, are assessed. [ABSTRACT FROM AUTHOR]