Mina Touzandejani; Alireza Soffianian; Norollah Mirghafari; Mohsen Soleimani
Abstract
Introduction: All living organisms, such as plants, animals and humans depends on the water and life may exist in a place where water is available. Groundwater is the main source of drinking water for more than 5.1 billion people around the world, especially in arid and semi- arid regions such as Iran. ...
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Introduction: All living organisms, such as plants, animals and humans depends on the water and life may exist in a place where water is available. Groundwater is the main source of drinking water for more than 5.1 billion people around the world, especially in arid and semi- arid regions such as Iran. Currently, groundwater provided about 60 percent of the worlds drinking water and 77.8 percent of the Iran's drinking water. In recent years, it has been found that groundwater quality is also important as much as its quantity. Nowadays, pollution of groundwater resources from pollutants, especially heavy metals reduces the quality of these resources. Heavy metals are one of the most important environmental pollutant that its entering into the water is raised by agricultural activities, industrial and urban development. Among the heavy metals, arsenic is a toxic and carcinogenic metalloids which are widely distributed in the environment and it has a twentieth abundance of elements in the Earth's crust with an average of 1.8 mg kg-1. Arsenic has been classified in the first group of cancer-causing compounds. It has different effects such as horny skin, liver, skin and bladder cancer, mental disorders, damage to neurons, blood pressure, lower IQ and reducing white blood cells and red blood. The Maximum permissible arsenic in drinking water is 10 micrograms per liter which has been identified by the World Health Organization and America Environmental Protection Agency. According to national standards of Iran, limitation of arsenic in drinking water is 10 micrograms per liter. So far, numerous studies were done to evaluate the environmental contamination of heavy metals, especially arsenic using geostatistical methods. The aim of this study was to evaluate the quality of groundwater in terms of Arsenic pollution.
Materials and Methods: study area is Hamedan - Bahar aquifer with an area of 800 square kilometers that is located on the northern slopes of Alvand Mountains. The central part of Hamadan city, Lalejin, Saleh Abad and Bahar city is located in the study area. To conduct this study, concentrations of arsenic was investigated in 94 groundwater points. To determine the spatial distribution of arsenic, different geostatistical methods were used. Then the results of this methods were compared using cross validation technique and MAE & MBE index and the most suitable method was chosen for this purpose. Eventually RBF method by multiquadric model was used. Moreover Contamination probability map was developed using indicator kriging models.
Results and Discussion: Arsenic concentrations were in the range between 5 – 79.5 micrograms per liter. Also The average concentration was 12.4 micrograms per liter. While the threshold for arsenic in water defined 10 micrograms per liter by the World Health Organization (WHO). So an average of arsenic in ground water is higher than limits of international standard. The spatial correlation analysis showed that the concentrations of arsenic in groundwater have no strong spatial dependency. So, for zoning this variable, between the nonparametric methods, radial basis function (RBF) by Multiquadric model was used. This method had lowest MAE and MBE index for arsenic in groundwater. The highest concentration of arsenic was in the industrial zone in the north of Hamadan (Hamedan, Tehran road). In general Excessive concentrations of arsenic are visible in the three areas : The first area is between Hamedan and Tehran Road Industrial Estate, that the high rate of abnormalities was found in this area (79.5 μg/L). Also the suburbs of Saleh-Abad and the Bahar city has high arsenic concentration. In these areas, groundwater levels were high and pollutants can penetrate more easily. The results of the contamination map using an indicator kriging method showed that 21.18% of aquifer moderately contaminated and about 10.9% of the aquifer area have a high contamination possibility. Polluted groundwater is matched with agricultural land especially the potato fields.
Conclusion: The results showed that the average concentration of arsenic in groundwater of Hamedan-Bahar basin is more than WHO and Iran department of environmental guidelines. The highest concentration of arsenic in agricultural lands and consequently in groundwater resources is due to the existence of polluting industries, the geological structure of the area where arsenic concentration naturally is high, cultivation of potatoes and other crops in the region and indiscriminate use of pesticides and chemical fertilizers in agriculture.
S.M. Hosseini-Moghari; Sh. Araghinejad
Abstract
Introduction: Due to economic, social, and environmental perplexities associated with drought, it is considered as one of the most complex natural hazards. To investigate the beginning along with analyzing the direct impacts of drought; the significance of drought monitoring must be highlighted. Regarding ...
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Introduction: Due to economic, social, and environmental perplexities associated with drought, it is considered as one of the most complex natural hazards. To investigate the beginning along with analyzing the direct impacts of drought; the significance of drought monitoring must be highlighted. Regarding drought management and its consequences alleviation, drought forecasting must be taken into account (11). The current research employed multi-layer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), radial basis function (RBF) and general regression neural network (GRNN). It is interesting to note that, there has not been any record of applying GRNN in drought forecasting.
Materials and Methods: Throughout this paper, Standard Precipitation Index (SPI) was the basis of drought forecasting. To do so, the precipitation data of Gonbad Kavous station during the period of 1972-73 to 2006-07 were used. To provide short-term, mid-term, and long-term drought analysis; SPI for 1, 3, 6, 9, 12, and 24 months was evaluated. SPI evaluation benefited from four statistical distributions, namely, Gamma, Normal, Log-normal, and Weibull along with Kolmogrov-Smirnov (K-S) test. Later, to compare the capabilities of four utilized neural networks for drought forecasting; MLP, ANFIS, RBF, and GRNN were applied. MLP as a multi-layer network, which has a sigmoid activation function in hidden layer plus linear function in output layer, can be considered as a powerful regressive tool. ANFIS besides adaptive neuro networks, employed fuzzy logic. RBF, the foundation of radial basis networks, is a three-layer network with Gaussian function in its hidden layer, and a linear function in the output layer. GRNN is another type of RBF which is used for radial basis regressive problems. The performance criteria of the research were as follows: Correlation (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE).
Results Discussion: According to statistical distribution analysis, the optimal precipitation distribution in many cases was not Gamma distribution. The various time-scales of SPI revealed that, at least in 50% of the events, Gamma was not the selected distribution. Throughout the drought forecasting on the basis of SPI time-series with four aforementioned networks, 80% of the data was allocated to the training process whilst the rest of them considered for the test process. The proper parameters of the networks were chosen via trial and error. Moreover, Cross-validation was used to overcome the over-estimation. The results revealed that the long-term SPIs outdid the others. Performance of the networks promoted with increases in time scales of SPI. In other words, the performance criteria improved proportional to the increases in the time-scales. Based on the Table 3, the least and best performance were contributed to SPI1 and SPI24, respectively. In this regard, R2 of MLP for observed and estimated values of SPI vitiated from 0.009 to 0.949. Similar to MLP, correlation of ANFIS, RBF, and GRNN increased from 0.021 to 0.925, 0.263 to 0.953, and 0.210 to 0.955. Comparison of observed and estimated mean values via Z test indicated that null hypothesis of equal mean observed and estimated values was only rejected for SPI1 with α=0.01. Hence, except SPI1 forecasting, the all other scenarios have remained the mean of observed time series which highlighted the robustness of artificial intelligence in drought forecasting.
Conclusion: The main objective of the ongoing research was monitoring and forecasting of drought based upon various time scales of SPI. In doing so, the precipitation data of Gonbad Kavous station during the period of 1972-73 to 2006-07 were used. Based on K-S test, the best statistical distribution test for different time scales of SPI evaluation was chosen, and then, the SPI was calculated based on the most fitted distribution. After generating the time-series, MLP, ANFIS, RBF, and GRNN were applied for drought forecasting. According to the findings, the lowest performance of forecasting belonged to SPI1 where its RBF’s best performance for R2, RMSE, and MAE were 0.263, 0.806, and 0.989. Furthermore, increases in SPI time-scale promoted the performance of networks. Thus, the worst and best performance belonged to SPI1 and SPI24, respectively. Among the utilized models, ANFIS stood superior to the others, and GRNN followed up after it.
