matlab software

Delivery discharge to each outlet, the number of outlets grouped in a block, and the sequence of water delivery to the outlets are the three main components of water delivery in irrigation networks. Therefore, scheduling the distribution and delivery of water in the network means a determination of these three components. In this research, an optimal water delivery model was developed. The proposed approach was applied to the M canal of the Moghan Irrigation Network of Iran. By choosing a certain number of blocks, the outlets located in the distribution canal are distributed inside each block in such a way that all the outlets are placed inside the blocks and no block is left without outlets. In the following, the way of scheduling water delivery to each of the outlets located in each block and the flow rate delivered to them is such that the flow rate of the canal, the time required to complete the irrigation scheduling, and also the difference in the volume of water delivered to each outlet are minimized. The limitations of the model are: a) the total flow entering the outlets that collect water at the same time should not exceed the capacity of the canal. b) the total water intake time of the outlets inside each block should not exceed the irrigation interval. In this research, the allocation of water and the cultivated area in the optimal water allocation model, which is linked to the crop model, were used to optimize water delivery.

Delivery discharge to each outlet, the number of outlets grouped in a block, and the sequence of water delivery to the outlets are the three main components of water delivery in irrigation networks. Therefore, scheduling the distribution and delivery of water in the network means the determination of these three components. In this research, an optimal water delivery model was developed. The proposed approach was applied to the Canal M of the Moghan Irrigation Network of Iran. By choosing a certain number of blocks, the outlets located in the distribution canal are distributed inside each block in such a way that all the outlets are placed inside the blocks and no block is left without outlets. In the following, the way of scheduling water delivery to each of the outlets located in each block and the flow rate delivered to them is such that the flow rate of the canal, the time required to complete the irrigation scheduling,g and also the difference in the volume of water delivered to each outlet are minimized. The limitations of the model are: a) the total flow entering the outlets that collect water at the same time should not exceed the capacity of the canal. b) the total water intake time of the outlets inside each block should not exceed the irrigation interval. In this research, the allocation of water and the cultivated area in the optimal water allocation model, which is linked to the crop model, were used to optimize water delivery.

By using the volume of water allocated to each crop and cultivated area, the water demand for each outlet in different time steps was obtained. Following determining the water demand of each outlet and also by knowing the physical characteristics of the canal, optimal water delivery modeling was done in the canal for each time step by using the optimal water delivery model and genetic algorithm. Water delivery factors are presented for the 17th time step (peak demand period). The results show that the optimal flow does not exceed the minimum and maximum flow of each off-take. Also, the shorter total duration of the exploitation process than the irrigation interval indicates compliance with the relevant condition in the model. The hydrograph of the inflow into Canal M showed that the maximum flow of the inflow into the canal is less than the capacity of the canal, which indicates compliance with the relevant condition in the model. The hydrograph of the inflow to the canal provides the number of settings of the main off-take of the distribution canal. During the completion of the water delivery program, the main off-take of the distribution canal, which receives water from the main Canal A, is adjusted a total of 23 times by the network operator.

In this research, the optimization model of water distribution and delivery in distribution canals was developed using a genetic algorithm in MATLAB software. With the aim of optimal allocation and delivery of water at different levels of the irrigation network and by combining the crop model, the results and output of the models were combined with each other so that the results of optimal water scheduling are more appropriate with the real conditions and water requirements of the network. For the canal in the peak demand period, the maximum and minimum canal capacity were calculated to be 2.573 and 0.590 m3.s-1, respectively, the maximum time to complete the irrigation program was 232 h, and the number of settings of the main off-take was calculated as 23 settings. The obtained results indicate that the developed models are useful for scheduling the optimal allocation and delivery of water and with its help different goals can be optimized simultaneously.

In the present paper, crop pattern criteria have been evaluated relying on sustainable development to increase agricultural water productivity. Seven criteria were selected as the main environmental and economic criteria and were prioritized and reviewed for important and strategic products in the Tajan catchment of Mazandaran province. Criteria prioritization was done using optimization through a genetic algorithm with an objective function based on sustainable development. Then, physical and economic productivity indices were calculated to determine the productivity value. Based on the results, in the selection of the crop pattern, firstly, the category of economic criteria and finally the category of environmental criteria have been given attention to the farmers in the current situation. But in the genetic optimization algorithm, all priorities have a similar order from the environmental point of view and then from the economic point of view although each product has its order of criteria. By this prioritization, the parameters of the cultivated area, the volume of water consumed, and the amount of chemical fertilizers have decreased on average by 26%, 34%, and 21%, respectively, and the parameters of product performance and profitability have increased by 43% and 61%, respectively. In addition to providing environmental standards and increasing sustainable development, this prioritization causes an average increase in physical productivity by 84% and an increase in economic productivity by 72%.

The excessive groundwater pumping and different hydraulic and geotechnical soil properties due to the stratified soil around pumping wells are of the most important factors on the land subsidence. Some consequences of land subsidence include the formation of cracks on the ground surface, destruction of buildings, damage to water conveyance pipelines embeded in the ground, the change in the longitudinal river steepness and vulnerability of residential areas to flood events due to lower soil permeability caused by the land subsidene. Because of the widely use of GIS to the evaluation of earth properties, many researches have been conducted so far on the application of GIS to land subsidence analysis. In this regard, a number of GIS-based researches focus on the application of data derived from In-sar radar to monitor the land subsidence without any geotechnical analysis. Due to the necessity of geotechnical analyses to more accurately approximate the land subsidence, the researchers proposed a method based on the influence function in order to relate the variation of phreatic groundwater surface around the pumping wells to the deformation of ground surface causing the land subsidence. Despite the incorporation of soil stratification effect, the inattention to variations of phreatic surface in variable pumping conditions from multiple wells and the mutual effects of wells on each other is accounted as a weakness of the proposed method which is addressed in the present study.

In the present research, a practical computer program (so-called CALS-SVD) was developed which initially imports the physical and hydraulic properties of an aquifer from GIS including the longitude and lattitude of wells, the specific storage coefficient of the soil, hydraulic conductivity, initial void ratio of the soil, soil compression index, the wet and saturated specific weights of the soil. Then it applies a new method integrating Influence Function with equations of varied pumping-induced influence radius of groundwater surface to estimate the subsidence (from multiple wells in a given time). Finally, the results of computations are added as new information layers to the imported GIS file. The developed computer model was applied to a case study in west of Guilan province located in the road connecting Saravan to Fouman. The applied approach in the studied area was to divide it into two regions. Then, 6 scenarios were considered in terms of reported pumping rates (ranging 30 to 120 m3/d). The maximum reported pumping rate in the region (120 m3/d) and the half and quarter of the maximum pumping discharge were applied to the wells located in two mentioned regions in order to model the land subsidence in the entire studied area. Taking into account the overlap of pumping wells, one of the wells located in the middle of others was selected as the origin and the effect of all the wells on each other were determined based on the relative distance derived from their longitudes and lattitudes.

The scenario A120B120 incorporating the maximum pumping discharge at the both divisions of the studied area used to validate the model. Validation of the obtained results with the field surveys shows an admissible performance of the presented software. Additionally, the results showed that the pumping-induced subsidence up to 30 cm around the wells leads to the expansion of subsidence area in the region. But a greater subsidence depth has negligible influence on the subsidence area, and it will be limited to 70 percent of the total surface area of the region. Meanwhile, the higher the pumping rate, the greater will be the influence radius. But the rate of increase in influence radius decreases for higher pumping values. Thus the average influence function will be limited to 105 meter at the studied area. According to the results, there is a linear relationship with an admissible correlation coefficient between the average groundwater pumping rate (in a 10 year period) and the maximum subsidence depth. Moreover, the maximum subsidence depth is highly affected by the stratification of the soil. Whereas the subsidence increase rate with pumping is mostly affected by the percentage of increase in pumping. Finally, the application of presented subsidence model in the studied regions indicates lower magnitude and rate of subsidence in a 10 year period in comparison with the results obtained from Damghan in a 5 year period.

Urban and industrial wastewaters are considered as the most contaminant of surface water. Entrance   of these pollutants to the river reduces the concentration of dissolved oxygen and aquatic life will be threatened. So, one of the main qualitative characteristics of water resources management is the concentration of dissolved oxygen. The base of the   developed model in this investigation is the convection- diffusion equation in soil. Terms of production and decay of dissolved oxygen were added to this equation. The final equation was discretized using the finite difference method with the implicit scheme. With applying the initial and boundary conditions, the equation set was solved by the Thomas algorithm. The calculations were done by programming in the MATLAB software. For the calibration and validation of the model, data obtained from two reaches of Zayanderoud River, including steel melt and Mobarakeh Steel factories, were used. The temporal and spatial variations of the dissolved oxygen were plotted and compared with the real data and the results of the MSP and CSP models. The results showed that the concentration of the dissolved oxygen could be well predicted through solving convection-diffusion equation with introducing two terms for the decay and production of oxygen. The comparison between the results of the model and two other models showed that the model led to better results in comparison to the MSP and CSP models.

Distribution pattern of the river bed particles grading creates important issues in investigation of the hydraulic, geomorphological and ecological behavior of the river.. For example, surface grain-size variability is crucial for illustrating sediment transport (Hoey and Ferguson, 1994; Russ, 1999; Joyce et al., 2001).
Particle size characteristics that are dependent on particle size distribution are estimated in different ways, such as sieving method, sampling techniques in the field, photographic print method, and the other methods that have been suggested so far (Aberle and Nikora, 2006). The most prevalent method is the sieving method that obtains particle size distribution curve using cumulative weight of passing aggregation,.. It is obvious that, measurement of the particle size distribution based on field methods are time consuming, overwhelming and non-economic, so developing a fast and accurate method for measuring the particle size distribution of the river bed has a significant effect on civil and environmental engineering. Nowadays, this process is possible using image processing methods to automatically extract particle size using digital images of river bed. Various methods have been reported which aim to provide robust and automated estimates of grain size from images, falling under two broad categories classified by (Buscombe et al., 2010) as, respectively, ‘geometrical’ and ‘statistical’. Both techniques require imagery where the smallest grains are resolved by at least a few pixels. Statistical methods characterize grain size using a measure sensitive to image texture. These approaches have used autocorrelation (Warrick et al., 2009), semi variance or one of the several other methods, including fractals (Buscombe, 2013) and grey-level co-occurrence matrices. Geometrical methods use image processing techniques (principally, threshold and segmentation) to isolate and measure the visible axes (or portions of whole axes) of each individual grain (e.g. Graham et al., 2005; Chang &Chung, 2012). This research showed that new methods of image processing have an adequate potential to replace with previous traditional methods. Also, the percentage of human bias in methods such as field sampling or Sticky layer is very high for sampling of the Armor layer of the river bed, while the image processing method has enough power to take all the details and analysis of the data and it has a desirable accuracy compared to traditional methods.
This research, with using image processing toolbox in MATLAB software, tried to improve the detection filters and finally determined the size distribution of sedimentary particles of armor layer in the alluvial river bed.