Irrigation
M. Mohammadi Ghaleni; H. Kardan Moghaddam
Abstract
IntroductionThe water quantity and quality has always been one of the main challenges in the issue of allocating water resources for different uses. Water quality management requires the collection and analysis of large amounts of water quality parameters that will be evaluated and concluded. Many tools ...
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IntroductionThe water quantity and quality has always been one of the main challenges in the issue of allocating water resources for different uses. Water quality management requires the collection and analysis of large amounts of water quality parameters that will be evaluated and concluded. Many tools have been found to simplify the evaluation of water quality data, and the water quality index (WQI) is one of these widely used tools. In summary, the WQI can be defined as a number obtained from the combination of several quality parameters based on standards for its extraction. The aim of this study was to develop and introduce the new Surface water Drinking Water Quality Index (SDWQI) adopt the water quality parameters measured on hydrometric stations of Iran. In developing this index, criteria such as the availability of required parameters in most rivers and simple and accurate methods have been considered. Also, the ability to calculate with the minimum general parameters of water quality, simple calculations and in terms of the international standard WHO for drinking is one of the advantages of the introduced index.Materials and MethodsFor this purpose, 12 water quality parameters including Total Dissolved Solids (TDS), Electrical Conductivity (EC), Total Hardness (TH), pH, Chloride (Cl-), Sulfate (SO42-), Carbonate (CO32-), Bicarbonate (HCO3-), Magnesium (Mg2+), Sodium (Na+), Calcium (Ca2+) and Potassium (K+) have been used from Rudbar and Astaneh hydrometric stations located on Sefidroud river. Then initial preprocessing on data e.g. correlation analysis, and multivariate statistical methods including cluster analysis (CA) and principal components analysis (PCA) are used to selecting and weighting of water quality parameters using the “clustering” and “factoextra” packages in R 4.1.1. In order to develop the SDWQI were performed four steps including, parameter selection, sub-indexing, weighting and aggregation of the index. Also, in order to evaluate the index of the present research, the results of the SDWQI have been compared with the WHO drinking water quality index and Schoeller drinking water quality classification.Results and DiscussionCorrelation analysis between water quality parameters shows a significant correlation between TDS, EC and TH parameters and also with Cl-, Ca2+ and Mg2+ parameters at the level of 1% in both Astaneh and Rudbar stations. On the other hand, the lowest values of Pearson correlation coefficient are related to pH and CO32- parameters with other quality parameters. The results of CA indicate that most of the water quality parameters are located in separate clusters. So only the parameters TDS, EC, Cl- and Na+ in both Rudbar and Astaneh stations are in the same cluster. The weights of the parameters showed that TDS and K+ are assigned with the highest and lowest weights equal to 0.163 and 0.031 based on PCA method. Also, PCA results show that first and second principal components covered 59.3% and 67.6% of the total variance of measured water quality parameters in Rudbar and Astaneh stations, respectively. Water quality classification results indicate that (40.5%, 16.4% and 23.7%) and (90.1%, 73.1% and 57.3%) of data in Rudbar and Astaneh stations, respectively, fell into the excellent and good categories for drinking purposes based on Schoeller classification, WHOWQI and SDWQI.ConclusionGenerally, the comparison of the SDWQI with the WHO index and the Schoeller classification shows the rigidity of the new index in the classification of water quality for drinking purposes. Each water quality index developed in order to evaluate the uncertainty of results, should be tested for data with different characteristics in terms of the range of variation with different limit values (minimum and maximum). The index developed in the present study is no exception to this rule and in order to better evaluate the results, it is suggested that to be evaluated and analyzed with data from other hydrometric stations. Another important points that should be considered in using any water quality index, including the present research index, is to examine the allowable limits of water quality parameters that are not considered in these indicators. The results of the study indicated that, two most important steps in the development of a quality index that have a great impact on its results are sub-indexing and weighting of parameters. According to the results, two ideas recommended for future research. One, choosing an appropriate method such as non-deterministic (fuzzy) and intelligent (machine learning) methods to sub-index the parameters and two, to weigh the parameters more effectively, multivariate statistical methods such as clustering, factor analysis and principal component analysis should be used.
M. Mohammadi Ghaleni; O. B ozorg Hadad; K. Ebrahimi
Abstract
Abstract
The Muskingum method is frequently used to route floods in Hydrology. However, application of the model is still difficult because of the parameter estimation’s. Recently, some of heuristic methods have been used in order to estimate the nonlinear Muskingum model. This paper presents a ...
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Abstract
The Muskingum method is frequently used to route floods in Hydrology. However, application of the model is still difficult because of the parameter estimation’s. Recently, some of heuristic methods have been used in order to estimate the nonlinear Muskingum model. This paper presents a efficient heuristic algorithm, Simulated Annealing, which has been used to estimate the three parameters nonlinear Muskingum model. The results show the high accuracy of the algorithm in estimation of the parameters, so that it is obtained terms of the sum of the square of the deviations between the observed and routed outflows (SSQ), the sum of the absolute value of the deviations between the observed and routed outflows (SAD), deviations of peak of routed and actual flows (DPO), and deviations of peak time of routed and actual outflow (DPOT), 36/78, 23/44, 0/9 and 0, respectively. As Value of the SSQ has obtained equal its value Harmony Search method that is the best answer between the heuristic Optimization Algorithms that has been used so far. Finally, the performance of the new proposed method has been compared with other methods. The results showed that the height efficiency of the algorithm in parameter optimization of the nonlinear Muskingum model. SA algorithm in the second example the Karun River flood test and the results were compared with the GA method. The results showed that SA algorithms estimate is better than the GA method. As the error sum of squares (SSQ) before 4947/06, the total absolute error (SAD) against 412/8, Dubai actual peak was 1182 cubic meters per second and peak Routing 1191 was obtained by the difference of these two (DPO) times less a percentage error and the occurrence of different steps in Dubai when the real peak and has Routing (DPOT) zero respectively. Finally, this research capability in the blank verses optimal SA algorithm making Muskingum model parameters indicated therefore, to use SA algorithm in this area is recommended.
Keywords: Flood Routing, Muskingum Model, Optimization, Simulated Annealing Algorithm
