Irrigation
H. Shokati; Z. Sojoodi; M. Mashal
Abstract
Introduction Arid and semi-arid climates prevail in Iran. The precipitation is also sparsely distributed in most areas of the country. Therefore, there is a need for management measures to overcome the water crisis. One of these measures is designing rainwater harvesting systems that can meet some ...
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Introduction Arid and semi-arid climates prevail in Iran. The precipitation is also sparsely distributed in most areas of the country. Therefore, there is a need for management measures to overcome the water crisis. One of these measures is designing rainwater harvesting systems that can meet some of the non-potable needs and reduce runoff in urban areas. The main components of rainwater harvesting systems in residential regions include the catchment area, storage tank, and water transfer system from the catchment area to the tank. The storage tank is the biggest investment in a rainwater harvesting system, as most buildings are not equipped with a storage system. Therefore, tank capacity should be determined optimally to minimize project implementation costs. The stored water volume and the project profit increases with increasing the tank volume. However, in this case, the price of the tank increases. Therefore, the tank capacity should be optimally designed to justify economic exploitation.Materials and Methods In order to evaluate the feasibility of using rainwater harvesting systems, the tanks’ volume was optimized. Due to the higher rainfall of Ardabil relative to the average rainfall of the country, it is expected that this area has a good potential for the implementation of rainwater harvesting systems. Therefore, this region was selected as the study area under the scenario of a residential house with 100 and 200 m2 catchment areas and four inhabitants. The amount of rainfall in the region is one of the primary parameters in determining the volume of rainwater collection tanks. Some of the precipitated water is always inaccessible due to evaporation from the surface. Nonetheless, since there is almost no sunlight during and immediately after rainfall, and also the received water enters the reservoirs shortly after precipitation, evaporation was assumed to be zero. Daily precipitation data for 42 years (from 1977 to 2019) were retrieved from the Ardabil Meteorological site. The daily water balance modeling method was used to analyze the rainwater harvesting systems due to the simplicity of interpretation, high accuracy and better general acceptance. Daily precipitation data were entered into this model and used as the primary source to meet the domestic demands. Simulation of rainwater harvesting systems was performed using daily precipitation data in MATLAB software, and the reliability of these systems was calculated for different tank volumes. Then, considering the price of drinking water and the current price of tanks in the market, the optimal volume of tanks was calculated using the Genetic Algorithm. Finally, the annual volume of water supply and the amount of water savings in case of using the optimal volumes of tanks were also estimated.Results and Discussion The results showed that the percentage of reliability is directly related to the volume of the tank, thus, the reliability percentage also increases with increasing the tank capacity. As the volume of the tank increases, the slope of the increasing reliability percentage decreases continuously, to the point that this slope becomes almost zero. Comparing the reliability percentage obtained for 100 and 200 m2 rooftops indicated that 200 m2 rooftop had a higher reliability percentage than 100 m2 rooftop due to receiving much more rainfall and meeting the water need for a longer duration. By comparing the results of overflow ratio for 100 and 200 m2 rooftops, it can also be concluded that using a fixed size tank, the overflow in 200 m2 rooftop is higher, which is due to receiving more water volume than 100 m2 rooftop. The results also showed that by using a 5 m3 tank, 44.5 and 24 m3 of water can be stored annually from the 200 and 100 m2 catchment areas, respectively, these are equal to 28 and 19 m3, respectively, if 1 m3 tank is used. The optimal tank volumes for 100 and 200 m3 rooftops are equal to 0.59 and 1.66 m3, respectively. Since the tanks are made in specific volumes, the obtained volumes were rounded to the closest volumes available in the market. Thus, a 1.5 m3 tank was used for a 200 m2 rooftop and a 0.5 m3 tank was applied for a 100 m2 rooftop.ConclusionApplication of a tank of 0.5 m3 for the rooftop of 100 m2 was the most profitable for saving 17% of water consumption, annually. Moreover, the optimal tank volume for the 200 m2 rooftop was selected to be 1.5 m3, saving about 32 % of water consumption per year. Water-saving percentages indicate the high potential of our study area for the implementation of rainwater harvesting systems.
mahboobeh farzandi; Seyed Hossein Sanaeinejad; Bijan Ghahraman; Majid Sarmad
Abstract
Introduction: Temperature and precipitation are two of the main variables in meteorology and climatology. These are basic inputs in water resource management. The length of the statistical period plays a pivotal role in the accurate analysis of these variables. Observation data at Iran's first ...
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Introduction: Temperature and precipitation are two of the main variables in meteorology and climatology. These are basic inputs in water resource management. The length of the statistical period plays a pivotal role in the accurate analysis of these variables. Observation data at Iran's first synoptic station from 1330 (1951) is available at the Iranian Meteorological Organization website The historical monthly precipitation and temperature of five stations in Iran is available since 1880 with missing data. These data measured by the Embassy of the United States and Britain from the Qajar period and recorded in World Weather records books. These synoptic stations include Mashhad, Isfahan, Tehran, Bushehr, and Jask. The monthly missing data were predominantly recorded during World War II (1941-1949). Unfortunately, these data have missing. Therefore, the accuracy of simulating these variables is very important. The current research aimed to predict the missing values of monthly temperature and precipitation in Mashhad station. The stations in the neighboring countries were selected due to the distance to Mashhad, relationship, and completeness of data since 1880, as the predictive variables. Monthly precipitation of Ashgabat from Tajikistan and Sarakhs, Kooshkah, Bayram Ali, Kerki and Repetek from Turkmenistan were selected as an independent variable in the making of Missing Rainfall in Mashhad. Also, the temperature of Ashgabat, Bayram Ali, Gudan, Sarakhs, and Tajan were selected to restore the monthly temperature of the Mashhad station. This research has fitted ten multiple regression models to monthly rainfall of Mashhad station and has fitted 6 multiple regression to the monthly temperature of Mashhad. then the parameters of these patterns are optimized by genetic and Ant Colony algorithm. Also, the Artificial Neural Network (MLP) model and Support vector regression have been selected and implemented in order to simulate monthly precipitation and temperature data of Mashhad.
Materials and Methods: In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). Genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover, and selection. Ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Artificial neural networks are one of the main tools used in machine learning. As the “neural” part of their name suggests, they are brain-inspired systems which are intended to replicate the way that we humans learn. Neural networks consist of input and output layers, as well as (in most cases) a hidden layer consisting of units that transform the input into something that the output layer can use. They are excellent tools for finding patterns which are far too complex or numerous for a human programmer to extract and teach the machine to recognize. In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting).
Results and Discussion: At the first stage, several multiple regressions were fitted to monthly precipitation (with coefficients ranging from 0.63 to 0.81) and six patterns for monthly temperature (0.986-0.993). Afterward, GA and ACO were applied to improve the accuracy of the selected regression models by optimizing their parameters. At the next stage, ANN and SVR were used to estimate the monthly missing values separately. Finally, the results of the previous stages were compared using the root mean square error (RMSE), and the optimal models were applied to determine the missing values of monthly temperature and precipitation of Mashhad. The results showed that the Genetic Algorithm and Ant Colony increase the accuracy of the estimation of missing rainfall data significantly more than the previous methods. The lowest error criterion (RMSE) between regression patterns is 9.8 millimeters. By genetic algorithm, this criterion is reduced to 2.56 mm, and by ant colony algorithm to 2.559.
Conclusion: Comparison of the above methods in restoration temperature and precipitation shows that evolutionary methods (GA and ACO) are the best for estimating the missing monthly precipitation and machine learning methods (ANN and SVR) are the best to imputation missing data of monthly temperature.
nafise seyednezhad; Seied Hosein Sanaei-Nejad; B. Ghahraman; H. Rezaee Pazhand
Abstract
Regional analysis, estimating missing values, areal rainfall, estimating PMP and rainfall- runoff models in daily scale are essential in water resources and climatological researches. Modified inverse distance interpolation method based on Fuzzy Mathematics (MIDW-F) is a new, efficient method and independent ...
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Regional analysis, estimating missing values, areal rainfall, estimating PMP and rainfall- runoff models in daily scale are essential in water resources and climatological researches. Modified inverse distance interpolation method based on Fuzzy Mathematics (MIDW-F) is a new, efficient method and independent of complex preconceptions hypothesis. The purpose of this paper is applying the new interpolation equation for above essential needs by calibration the daily rainfall of Mashhad Plain catchment. Screening and normalizing distances and elevations were done, then effects of fuzzy operations (Max, Min, Sum, Multiplication and SQRT) are Checked out and optimizing the parameters of MIDW-F by Genetic algorithms. The 215 daily precipitations (49 rain gauge stations) were analyzed and were calibrated. The results showed that the best operators are Minimum (Share58%), multiplying (Share35%) and total contribution rate of others are 6%. The MIDW-F was compared with the three others conventional methods (the Arithmetic mean, Thiessen polygon and IDW) and results showed that the errors of MIDW-F method were reduced noticeably. Largest Regional Mean Square errors (RMSE) is for Arithmetic mean (Max. 90.45, Min. 5.76, variance 686.8 and 70% Cv) and smallest RMSE belong to MIDW-F (Max. 56.67, Min. 4.6, variance 340.92 and 57% Cv). Zoning of daily rainfall at 22/3/2009 and 23/2/2010 and with MIDW-F and IDW methods were conducted and evaluated. The results showed that the zoning by MIDW-F proposed more details. So this method\ is proposed for the interpolation of daily precipitation in a homogeneous region.
J. Soltani; A. Moghaddamnia; J. Piri; J. Mirmoradzehi
Abstract
Nowadays, accurate estimation of evaporation as one of the important elements of hydrological cycle can play an important role in sustainable development and optimal water resources management of the countries facing water crisis. Up to now, empirical methods and formulas on estimation of non-linear ...
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Nowadays, accurate estimation of evaporation as one of the important elements of hydrological cycle can play an important role in sustainable development and optimal water resources management of the countries facing water crisis. Up to now, empirical methods and formulas on estimation of non-linear and complex process of daily pan evaporation have been developed that is of uncertainty. These methods and formulas do not have high accuracy and also access to their input parameters is difficult or their measurement requires high cost and time. In this study, performances of two non-linear models of NN-ARX and ANFIS have been evaluated to estimate daily pan evaporation under arid and hot climate conditions including dry and warm climate (Iranshahr), dry and coastal warm (Chahbahar), and semi-arid and warm temperate (Saravan). For this purpose, the best combination of model inputs was selected by using Genetic Algorithm embedded in Gama Test software for each of Synoptic stations located in these regions for the 5years period(2005-2010), then daily pan evaporation was estimated by using NN-ARX and ANFIS models. By employing the statistical criteria including R2، RMSE and MAE, performances of ANFIS model with three Gaussian membership functions and NN-ARX model were evaluated for each of the selective Synoptic stations. The obtained results indicate the accuracy of ANFIS model is higher than the one of NN-ARX model in estimating daily pan evaporation in different climatic conditions.
H. Rezaei; J. Behmanesh; S. Besharat
Abstract
With respect to necessity of the optimum use of water resources and existence of many various optimization methods, in this study 3 kinds of heuristic algorithms have been used including Particle Swarm Optimization, Genetic Algorithm and Simulated Annealing to optimize the operation of Shaharchai dam ...
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With respect to necessity of the optimum use of water resources and existence of many various optimization methods, in this study 3 kinds of heuristic algorithms have been used including Particle Swarm Optimization, Genetic Algorithm and Simulated Annealing to optimize the operation of Shaharchai dam reservoir as an application. The optimization was carried out considering the probability of inflow for a period of 5 years. In order to obtain the best operation of reservoir, monthly release was defined as a second order polynomial according to storage volume and inflow, and different parameters of these algorithms have beenadjusted to minimize the objective function in which supplying the required demand of downstream was defined as the target. The best state of each algorithm is selected through 10 times running of programs (due to intrinsic random behavior of algorithms) and the results comparison leads to realization of which method can perform the best. According to the results, Particle Swarm Optimization method operates more effectively and produces the best results in solving reservoir operation problems. So as an application, control curves of release and storage volume have been extracted for Shaharchai dam reservoir using this method.
M. Aghajani; Maryam Navabian
Abstract
Water for rice cultivation is one of the main inputs. The new administration of irrigated rice is increase water efficiency and water conservation in the paddy fields. In this research, for optimization of intermittent irrigation management in proportion to water requirement of different stages of rice ...
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Water for rice cultivation is one of the main inputs. The new administration of irrigated rice is increase water efficiency and water conservation in the paddy fields. In this research, for optimization of intermittent irrigation management in proportion to water requirement of different stages of rice growth was present an optimization- simulation model to maximize irrigation water, transpiration and evapotranspiration productivity Indexes. Irrigation water depth in stages of tiller, vegetative, maturity, harvest and irrigation intervals were selected as decided values in optimization model. Simulation of plant growth stages, using the hydrological model SWAP and genetic algorithm was used to solve the optimization model to maximize agricultural productivity. Finally, the optimum amount of irrigation water productivity, transpiration and evaporation - transpiration were obtained 1.60, 2.90 and 1.33(kg/m3) respectively. Results showed, irrigation water productivity index has more harmonize with Sefidroud irrigation network. Also the index is user-friendly in applying and calculating. So according to maximizing of water productivity index irrigation depth was recommended 51, 29, 39 and 11 mm respectively in stages of tiller, vegetative, maturity, harvest and and 8 days period of irrigation intervals to improve water productivity index in Hashemi variety in Rasht. Optimization results showed optimal intermittent irrigation is successive compared with flood irrigation in rice.
