سید محمد حسینی موغاری

The increase in dust storms occurrence in recent years in southwestern Iran, especially in Khuzestan province, and consequently the decrease in air quality in these areas, has doubled the importance of forecasting and linking this phenomenon with climate variations. The aim of this study was to investigate the efficiency of hybrid Genetic-Annealing (GA-SA) and Genetic Algorithm (GA) methods for selecting optimal input variables in forecasting the frequency of days with dust storm (FDSD). For this purpose, hourly dust data and meteorological organization codes, as well as climatic data including maximum temperature, minimum temperature, average temperature, total rainfall and maximum wind speed on a seasonal scale with a statistical period of 35 years (1984-2018) in seven synoptic stations in Khuzestan province were used. Then, by having a time series of FDSD index and other climatic variables, it was compared to the efficiency of different modes of input variables, in order to forecasting the frequency of days with dust storm in the next season. The results showed that the hybrid Genetic-Annealing method (GA-SA) had the best performance among all the modes of selecting the input variables; In this case, the evaluation criteria of R, MAE and RMSE varied from 0.91, 0.29, and 0.44 to 0.99, 0.13 and 0.17 in the studied stations, respectively. Also, the average frequency of days with dust storm on a seasonal scale in the studied stations varied from 1.68 to 4.10, respectively, so that with increasing FDSD index in the study station, the predictive accuracy of all modes increased so that in the first forecast state (based solely on the FDSD index), the correlation coefficient between the observational values of the days associated with dust storms and its computational values increased from 0.87 to 0.95. For the second case (forecast based on frequency of days with dust storm and all Auxiliary Characteristics, ie FDSD & AC), the third mode (based on the optimization of the Genetic Algorithm) and the fourth mode (based on the hybrid Genetic-Annealing method) the correlation coefficient also varied from 0.93 to 0.94, 0.91 to 0.97 and 0.94 to 0.99 in the studied stations, respectively. In general, by comparing the modes used, the hybrid Genetic-Annealing method (GA-SA) performed the best, followed by the Genetic Algorithm (GA). The results of this study can be useful in managing the consequences of dust storms and desertification programs in the study areas.

Due to water use increasing, attention to optimal water resources allocation is needed. In recent decades, the use of intelligent evolutionary methods for optimization of water allocation was focused more by researchers. The aim of this study is to development on water resources planning model that determined the proper cultivation, optimal exploitation of groundwater and surface water resources although water allocation among crops is a way to minimize the adverse effects of dehydration and increase its revenue. In this study, for maximizing profits, estimating crop water requirements at different periods to optimize the management of cropping patterns and irrigation management in cultivation in Varamin irrigation network using a new evolutionary algorithm was called the water cycle. Then for validation of this method is that a new approach and ensure the integrity of its performance Its results are compared with a genetic algorithm model and linear programming as our base (R2=0.9963). The results showed that the area cropping pattern was not optimal and the area under cultivation of crops such as wheat, barley, tomatoes, Bamjan, melon, alfalfa reaches zero and the new paradigm of the largest area under cultivation to industrial goods and then was assigned cucumbers. While our revenues have increased about 11 percent. In addition to amount of water in different months remain in the network that can be used for many that such as injection into underground aquifers or other crops based on the amount of water available.

Drought monitoring is one of the main pillars of drought management. Therefore, investigators are always looking for a way to improve the drought monitoring accuracy. The main purpose of this paper is to investigate the efficiency of GRACE satellite observations in drought monitoring in the Markazi basin of Iran between 2002 and 2016. With the launch of this satellite, it has been possible to monitor total water storage anomalies (TWSA) for the entire world with high precision. Due to the significance of human activities impact on Total Water Storage Anomaly (TWSA), obtained from GRACE in the above mentioned basin, the common GRACE-based drought indices, such as Drought Severity Index (DSI), has not been efficient in this basin. Therefore, in this paper the newly Modified Drought Severity Index (MDSI) is introduced based on de-trended TWSA time series. Also, both Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were used, as the criteria to assess the performance of DSI and MDSI. The results showed that the correlation coefficient between DSI and SPI12/SPEI12 were equal to 0.42/0.26, while these values for MDSI were equal to 0.69 and 0.56, respectively. Calculated MDSI time series revealed that during the studied period, the most severe and longest drought occurred in the years 2008-09. During this period the basin faced with a deficit of 238 km3. On the basis of maximum and average rate of deficit changes in the basin, at least 21 and on average 91 months is needed for recovery of this deficit.

The lack of precipitation data has always been one of the major constraints on water engineering and climatology studies. Hence, researchers and engineers are always investigating alternatives to fill the gap of data scarcity for ungauged areas. Nowadays, one of the options for the researchers in this area is applying global gridded precipitation datasets. These datasets provide a gridded regional or global precipitation data by collecting data from various sources including gauges, remote sensing information, and numerical models. The first step to use the datasets is being aware of different sources of these datasets. The second step is to select the most suitable dataset for a given region. This selection is carried out by comparing the extracted precipitation data from different datasets with the existing gauged information. Given the necessity of using the global precipitation datasets, a brief description of the method of obtaining precipitation data in each dataset is presented in the current paper. Moreover, the characteristics of each database, including the spatial-temporal resolution, and time period of the information of databases has been explained.

The Standardized Precipitation Index (SPI) is the most common index for drought monitoring. Although the calculation of this index is usually done by using the gamma distribution fitting of precipitation data, studies have shown that for accurate monitoring of drought, the optimal distribution of precipitation in each month should be determined. On the other hand, in non-stationary time series, it is not possible to determine the proper parameters of probability distributions. Hence, in recent years, the non-parametric SPI has been introduced, in which the parametric distribution is replaced by an empirical probability distribution. However, in national studies, there is still insufficient attention to non-parametric drought indicators. Therefore, in this paper, the performance of the nonparametric SPI was evaluated for drought monitoring over Gorganroud basin during 1972 to 2015 and the results were compared with the parametric SPI. The SDAT software package was also introduced to calculate nonparametric drought indices. The results of this study showed that time series of the parametric approach based on the best probability distributions and the nonparametric approach are well correlated with each other. The correlation coefficient between the two-time series is equal to one. Drought events have been detected at the same time based on the two approaches. Based on the SPI12 in both parametric and non-parametric approaches, the longest drought occurred in the basin between February 1983 and March 1984 for a period of 14 months, during this period, the basin suffered severe drought.

Rivers are considered one of the most important resources of providing fresh water. Restrictions faced by such resources underline the necessity of preserving their quality. Water quality indices are usually resorted to qualitatively monitor water resources. Any of such indices is calculated with regard to a series of specific qualitative parameters. The process of sampling and quantification of the aforementioned parameters are, however, time-consuming and costly. Finding a reliable method comprised of minimum qualitative parameters could be, therefore, of great help in classifying water quality. As an alternative to the common NSFQWI, the advantages of the Probabilistic Neural Network (PNN) as a classifier are used in the present study to classify water quality of Karun River. In order to fulfill this objective, the qualitative statistics of 172 samples were used in a way that qualitative parameters and water quality classes derived from NSFWQI are used respectively as the input and output of the model. The assessment criteria of error rate, error value, accuracy and Spearman’s correlation coefficient were used to evaluate the performance of PNN model. The results showed that through making use of merely three parameters of turbidity, fecal coliform and total dissolved solids, PNN model is capable of classifying water quality with the accuracy of 94.365% and 90.7769% at two stages of training and test respectively which in turn indicates considerable accuracy of PNN in determining water quality classification.

The present study aims to analyze the accuracy of four global gridded precipitation datasets namely CRU, GPCC, PCDR, DEL over Lake Urmia basin, Iran. To this end, observed data have been collected from six synoptic stations namely Urmia, Mahabad, Takab, Tabriz, Maragheh and Saqqez over a period of 30 years (1984-2013). Evaluation was done based on Nash-Sutcliffe Efficiency (NSE), correlation coefficient (CC), Root Mean Square Error (RMSE) and Bias. Results on all scales (monthly, seasonal and annual) showed the proper performance of the GPCC. For example, in annual scale, GPCC, outperformed other databases with NSE, CC and RMSE values of 0.87, 0.94, and 31.12 mm respectively. According to the results, performance of the CRU is acceptable. This dataset has been performed better than the GPCC, based on Bias. Also PCDR''s and DEL''s performance were unacceptable in all time scales.

Nowadays, along with an increase in water needs, there is no balance between water demand and water supply in most regions of the country. Therefore, planning an appropriate policy to strike a balance between a declining water supply and an increasing demand to prevent a crisis is of utmost importance. The use of optimization methods in this context can be useful. Evolutionary algorithm methods are known as appropriate methods in this regard, and their suitable performance have been reported. Biogeography-based optimization (BBO) is a new evolutionary algorithm which its high performance in some aspects has been proved. The main objective of this study was to assess the performance of BBO in water resources management for the first time. Firstly, BBO was used for finding optimal points of three benchmark function including Sphere, Rosenbrock, and Bukin6; secondly, it was applied for an optimal operation of the Karon4 Reservoir with the aim of hydropower generation. In order to evaluate the performance of BBO, in addition to this method, the genetic algorithm (GA) and the nonlinear programming (NLP) were employed. The results of benchmark function showed that BBO delivered a better performance than the GA in finding the optimal points of three functions. Moreover, BBO reached an optimal solution with a higher degree of accuracy. In operation of the Karon 4 Reservoir, the results also indicated the high efficiency of BBO in extracting optimal operational policies in such circumstances; the objective function value of BBO at the best performance was 1.223, and, that for GA was 1.535. Furthermore, the global optimal solution obtained from NLP for this problem was 1.213.

Many algorithms and methods have been applied, so far, in order to operate reservoirs, optimally. Recently, the Cuckoo Optimization Algorithm (COA) as a new Evolutionary Algorithm (EA) has been utilized for solving a number of bench mark functions and real problems, and high performance and efficiency has been reported for solving difficult optimization problems. The main objective of the current study was an assessment of the COA efficiency in water resource systems in order to extract optimal operation policy for a reservoir with hydropower application. An application of this algorithm in a thirty-year operation of the Karun4 Reservoir was evaluated. The results were compared with those results of Genetic Algorithm (GA) outcomes. The results showed that the COA has a greater ability to reach optimal solution in comparison with the GA. The mean values of the objective functions were 6.34 and 9.28 for the COA and GA algorithms, respectively. Furthermore, the obtained results by the COA showed less difference to the global optimum.

Due to the importance of precipitation data in different fields and also lack of widespread and proper rain gauge networks, the precipitation data should be somehow estimated. Satellites are one of the best apparatus which can use to prepare the required precipitation data. However, it is essential to evaluate the accuracy of such data before using them. The main target of this paper is to evaluate the accuracy of CMORPH and TRMM 3B42RT V7 precipitation data, in Gorganrood Basin, IRAN. To achieve this aim, data of both above mentioned satellites are compared with observation data from six stations including; Tamr, Ramiyan, Bahlakeh, Dashlei, Gorgan Dam, Fazel Abad and Ghaffar Haji, for periods of 2003 to 2007. Moreover, different probability criteria such as; Correlation Coefficient, Mean Bias Error, Root Mean Square Error, CV (Coefficient of Variation) of Root Mean Square Error, Relation Bias Criteria, Probability of Detection, False Alarm Ratio and Critical Success Index were calculated for both satellites. Results show that the studied satellite’s data in monthly and seasonal scales are more accurate than daily scale. The highest correlation between TRMM 3B42RT V7 and CMORPH data comparing to their relevant observed data is related to the August precipitation in “Ghaffar Haji” station, which are equal to 0.93 and 0.85, respectively. Also, the results show that both TRMM 3B42RT V7 and CMORPH satellites estimated precipitation lower than observed data. Also, Probability of Detection and Critical Success of CMORPH satellites are better than TRMM 3B42RT V7. However, False Alarm of TRMM 3B42RT V7 satellite is better.

Lack of reliable long term data of precipitation, required for hydrometeorological studies, is a major challenge in most of weather stations of Iran. The present study aims to analyze the possibility of using global precipitation data as an alternative to the in situ observations. For this purpose, precipitation data collected from three global datasets namely APHRODITE, GPCC, and CRU were studied over a period of 30 years (1978-2007) for three synoptic stations located within Karkhe basin; Kermanshah, Khoram-Abad, and Hamedan. In addition to the total amount of precipitation the Standardized Precipitation Index (SPI) was also calculated using data retrieved from these three datasets. The obtained values were compared with observed ones. The obtained results revealed that different global databases perform differently in various climatic regions. However, in general, CRU at Kermanshah station, and APHRODITE at Khoram-Abad and Hamadan station, outperformed the others. For drought monitoring, based on annual SPI index, CRU by 83 percent of correct drought class recognition at Kermanshah station and GPCC by 83 and 70 percent of correct drought class recognition at Khoram-Abad and Hamadan stations, respectively, showed the best performance.

Precipitation is an important element of the hydrologic cycle and lack of this data is one of the most serious problems facing research on hydrological and climatic analysis. On the other hand, using satellite images has been proposed by many researchers as one of practical strategies to estimate precipitation. The present paper aims to evaluate the accuracy of satellite precipitation data, provided by PERSIANN and TRMM-3B42 V7 in Gorganrood basin, Iran. To achieve this aim, two sets of daily precipitation ground-based data, 2003 to 2004 and 2006 to 2007, from six stations of Gorganrood basin, named; “Tamer”, “Ramian”, “Bahalkeh-ye Dashli”, “Gorgan Dam”, “Ghaffar Haji” and “Fazel Abad” have been used in this paper. The evaluation indices have been calculated and analyzed in different time scales, including daily, monthly and seasonal. The results indicated that the two above mentioned satellite models are not accurate in daily scale. However, they showed reasonable accuracy in monthly and seasonal scales. The highest correlations between satellites and recorded data in daily and monthly scales, for TRMM-3B42 V7 in “Gorgan Dam” and “Bahlke Dashlei” stations, are 0.397 and 0.404, respectively. The comparison of measured and satellite data of winter showed better agreement for PERSIANN model. However, TRMM-3B42 V7 shows better correlation in other seasons. The results also indicated that while TRMM-3B42 data displays higher correlation with measured data, PERSIANN provids better results in predicting the number of rainy days.

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.
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 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.
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.

Groundwater resources are the main source of fresh water in many parts of Iran. Groundwater resources are limited in quantity and recently due to increase of withdrawal, these resources are facing great stress. Considering groundwater resources scarcity, maintaining the quality of them are vital. Traditional methods to evaluate water quality insist on determining water quality parameter and comparison between them and available standards. The decisions in these methods rely on just specific parameters, in order to overcome this issue, water quality indices (WQIs) are developed. Water quality indexes include a range of water quality parameters and using mathematical operation represent an index to classify water quality. Applying the classic WQI will cause deterministic and inflexible classifications associated with uncertainties and inaccuracies in knowledge and data. To overcome this shortcoming, using the fuzzy logic in water resources problems under uncertainty is highly recommended. In this paper, two approaches are adopted to assess the water quality status of the groundwater resources of a case study. The first approach determined the classification of water samples, whilst the second one focused on uncertainty of classification analysis with the aid of fuzzy logic. In this regard, the paper emphasizes on possibility of water quality assessment by developing a fuzzy-based quality index even if required parameters are inadequate.
The case study is located in the northwest of Markazi province, Saveh Plain covers an area of 3245 km2 and lies between 34º45′-35º03′N latitude and 50º08′-50º50′E longitudes. The average height of the study area is 1108 meter above mean sea level. The average precipitation amount is 213 mm while the mean annual temperature is 18.2oC. To provide a composite influence from individual water quality parameters on total water quality, WQI is employed. In other words, WQI is a weighting average of multiple parameters. The present research used nine water quality parameters (Table 2). In this paper Fuzzy Water Quality Indices (FWQIs) have been developed, involving fuzzy inference system (FIS), based on Mamdani Implication. Firstly, five linguistic scales, namely: Excellent, Good, Poor, Very poor, and Uselessness were taken into account, and then, with respect to ‘If→then’ rules the FWQIs were developed. Later, the seven developed FIS-based indexes were compared with a deterministic water quality index. Indeed seven FWQIs based on different water quality available parameters have been developed. Then developed indices were used to evaluate the water quality of 17 wells of Saveh Plain, Iran.
The present study analysed groundwater quality status of 17 wells of Saveh Plain using FWQI and WQI. Based on the driven results from WQI and its developed fuzzy index, similar performance was observed in most of the cases. Both of them indicated that the water quality in six wells including NO.1, 2, 6, 12, 13, and 17 were suitable for drinking. Due to the fact that the values of both indexes were under 100, the mentioned wells could be considered as drinking water supplies. The indexes illustrated the very poor quality of wells NO.7, 9, 10, 11, 14, and 16. As a result, according to FWQI1 along with WQI, nearly 35% of wells have proper drinking water quality, while approximately 30% and 35% of them suffered from poor and very poor quality, respectively. The overall picture of water quality within the study area was not satisfying, hence, an accurate site selection for discovering water recourses with appropriate quality for drinking purpose must be responsible authorities’ priority. Analysis of FWQI2, FWQI3 and FWQI4 revealed that elimination of the parameters slightly changed the result of FWQI2; however, FWQI3 and FWQI4 did not vary considerably. Thus, Cl influenced the water quality slightly, but Ca and K did not affect the water quality of the plain. The results showed that inexistence of one of the mentioned parameters would not affect the computational process adversely. A glance at FWQI5, FWQI6 and FWQI7 revealed the improper performance of FWQI5 to show wells’ water quality status. Throughout the FWQI5 evaluation process, all the wells’ water quality stood in Excellent category. Due to the considerable values of TDS in the Plain, elimination of this parameter in FWQI5 caused inappropriate evaluation. Hence, whenever a case study deals with a high value of a specific quality parameter, elimination of that parameter would negatively demote validation of the analysis. Figures (3)-(6) represented the results of WQI along with seven FWQIs for 17 utilized wells’ water quality assessment in the study area during the proposed periods.
Throughout the present study, the capability of seven FIS-based indexing procedures in modelling the water quality analysis of 17 wells of Save Plain was discussed. The proposed FWQIs were developed on the basis of Mamdani approach by applying triangular and trapezoidal membership functions to determine the groundwater quality of the case study according to the nine parameters. The results revealed that FWQI1-4 outperformed others. On the other hand, FWQI5-7 which eliminated three out of the nine parameters, did not made a valid contribution to the computational context. This might be related to omitting the effective water quality parameters from the inputs of the model. The results also illustrated that, only six out of 17 wells of the region could be considered as suitable sources for the drinking purpose. The water quality status in five wells was not satisfying, and six wells were plagued by very poor quality of water.

The largest amount of water in Iran is used in agricultural sector. Thus، efficient use of water in this sector will be significantly effective in maintaining water resources and optimum use of available water. In many regions، surface reservoirs are responsible for providing water to downstream agriculture. Optimal operation of reservoir is one of the major parts of surface water resource optimization. So far، several optimization approaches have been used، among them، the most popular methods are Evolutionary Algorithms. In this study، Firefly Algorithm (FA)، as a new method، was proposed for optimal operating of Bazoft reservoir. The operation modeling was carried out for a period of 120 months related to 1986 to 1995 years. The considered objective function was defined as minimizing the sum of squared differences between the demands and the release from the reservoir divided by maximum demand during operation. The model performance was examined compared to Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). These two mentioned algorithms are known as common and standard methods. The results indicated that firefly algorithm can better perform than other methods. The mean value of the objective function of this method was 0. 408، and the mean of the objective function for the GA and PSO were 0. 618 and 0. 913، respectively. In addition، FA has created less deficiency values and milder deficiency compares to GA and PSO.