فهرست مطالب a majnooni heris

Water plays a crucial role in ensuring the sustainable development of any region. Given that our country consists primarily of arid and semi-arid regions, where the majority of rivers are also found, along with the critical state of groundwater extraction and the growing importance of surface water, It is crucial to have a deep understanding of the future condition of water resources within the country's watersheds (Fathollahi et al., 2015). By utilizing intelligent models, it becomes feasible to represent the inherent relationships between data that cannot be solved by conventional mathematical methods. Support vector machine (SVM) and Random Forest algorithms are two types of machine learning methods that utilize essential algorithms for making repeated and accurate predictions (Kisi & Parmarm, 2016). The most recent study conducted by Zarei et al. (2022) evaluated the risk of flooding using data mining models of SVM and RF (case study: Frizi watershed). By analyzing the results, it was found that both the SVM algorithm and the new random forest algorithm showed higher accuracy in predicting flooding risks, both in terms of the educational data and algorithmic performance. The purpose of this study is to simulate the precipitation-runoff process in the hydrometric stations at the end of the Maragheh plain (Khormazard station on the Mahpari chai river and Bonab station on the Sufichai river) in East Azerbaijan province using support vector machine and random forest modeling algorithms. This study has been conducted over a period of 43 years, making it one of the few research cases in this area.

The Maragheh Sufi chai basin is situated in the eastern region of Lake Urmia, within the East Azarbaijan province. It covers an area of 611.89 square kilometers and is located between longitudes 45° and 40´ to 46° and 25´and latitudes from 37° and 15´ to 37° and 55´ north. The average height of the basin is 1767 meters above sea level (Sharmod et al., 2015). Based on the substantial changes observed in the runoff trend in the data since 1994 (without any noticeable change in the precipitation trend), the available data was divided into two distinct periods. The first period spans from 1976 to 1994, and the second period covers the years 1995 to 2019. To simulate rainfall-runoff, first the average rainfall of Maragheh plain was calculated by polygonal method. Subsequently, this data was combined with the discharge output from Bonab and Khormazard stations, with a one-day time lag. These inputs were then utilized in two models, SVM (kernel function) and RF. For this purpose, 70% of the data was used for the training stage and 30% of the data was used for the validation stage. Then, the rainfall and runoff training sets from one day before were chosen as the predictor variables, while the runoff training set was designated as the target variable. Several combinations of runoff and rainfall inputs were evaluated for the purpose of modeling. The inputs consist of the monthly Q and P values that were recorded previously (Pt, Qt-1), while the output represents the current runoff data (Qt), with the subscript t indicating the time step. As a result, two input combinations were constructed from Q and P data (as seen in Table 3) and SVM and RF models were used for rainfall-runoff modeling to determine the optimal input combination.
Calculating average rainfall through the Thiessen Polygons method Thiessen polygons, which are Voronoi cells, are used to define rainfall polygons that correspond to the surface area (Ai). These polygons are used to weight the rainfall measured by each rain gauge (ri). Consequently, the area-weighted rainfall is equivalent to:
(1)
Random Forest Algorithm
Random forest is a modern type of tree-based methods that includes a multitude of classification and regression trees. This algorithm is one of the most widely used machine learning algorithms due to its simplicity and usability for both classification and regression tasks.
Support Vector Machine (SVM) algorithm
Support vector machines works like other artificial intelligence methods based on data mining algorithm. The most important functions of the support vector machine model are classification and linearization or data regression.
       Evaluation Criteria
To evaluate the models and compare their effectiveness, this research employs metrics such as the root mean square error (RMSE), correlation coefficient (r), explanation coefficient (R2) and Nash-Sutcliffe efficiency coefficient (NS) are used. Below are the relationships among these criteria:
(2)
(3)                                                     
(4)
(5)

 Figure 6 displays the time series data for rainfall and runoff during the two study periods, before and after 1994.The analysis of the figures showed that for Bonab station, during the two study periods, the value of Kendall's statistic for precipitation variable was 0.044 and 0.028, respectively. For Khormazard station, this statistic value for the first and second period was 0.030, and 0.028, respectively. However, these values are not significant at the 95% level. This indicates that the annual rainfall for the two studied stations during these years is not statistically significant. Therefore, it is concluded that the annual rainfall in these stations between the years 1976 to 2019 did not show any significant trend. The variations observed during this period were deemed normal, suggesting that the time series of rainfall displayed fluctuating patterns. However, it should be noted that there were instances of both increasing and decreasing trends in certain years Examining the time series reveals varying trends Initially, the outflow from Bonab station (both a and b) displayed fluctuating patterns, followed by periods of both decreasing and increasing trends. However, in recent years, there has an increase in outflow from this station. The Mann-Kendall test statistic for the two study periods for this station is 0.325 and 0.512, respectively. These values are significantly different at the 95% level, indicating that the increasing trend of discharge for both time periods was statistically significant. The reason for this trend at the Bonab station, compared to other entrance stations to Lake Urmia, is the lower demand for water in the Sofichai basin for agricultural and industrial purposes, in contrast to other rivers. To explore the root cause of this issue, studies should be conducted to examine both underground and surface water sources, as well as the utilization of water in the agricultural and industrial sectors of this region. On the contrary, the trend observed at Khormazard station (c and d) is different. Unlike Bonab station, the discharge from Khormazard station exhibited a complete downward trend. The Mann-Kendall test statistic for the discharge variable during our two research periods were -0.269 and -0.412, respectively. At the 95% level, the decreasing trend of discharge in this station was found to be significant. On the other hand, it is apparent that the volume of discharge in this hydrometric station has decreased drastically since 1976 (d). Apart from 2007, when there was a sudden increase in discharge volume, the water inflow into lake Urmia has remained at its lowest level throughout the years. To analyze the Bonab and Khormazard stations during two distinct periods, rainfall and runoff statistics (average, minimum, maximum) for the first period (1976-1994) and the second period (1995-2019) are presented in Tables 4 and 5. Based on the data presented in both tables, the Bonab station displays the highest average rainfall and runoff values in the total data column, while the Khormazard station has the lowest average rainfall and runoff values.
As mentioned, in order to model rainfall-runoff data using SVM and RF models, a portion of the data was used for training purposes, while another portion was used for validation. Tables 5 and 6 present the values of the calculated statistical indicators associated with the results obtained from the training and validation sections for both SVM and RF models. According to the results of Tables 6 and 7, it is clear that in both study periods, the SVM model outperformed the RF model at the Bonab station. The SVM model demonstrated superior accuracy in simulating both flow rate and monthly rainfall. Conversely, at the Kharmazard station during these periods, the RF model displayed better performance compared to the SVM model. The modeling results in the test set for both stations revealed that the mutual correlation values for the first and second study periods at the Bonab station were 0.85 and 0.84, respectively. For the Kharmazard station, these values were 0.79 and 0.75, respectively.

The results indicate that for both periods at the Bonab station, the SVM model exhibited higher efficiency compared to the RF model. Conversely, at the Khormazard station, the RF model outperformed the SVM model for both periods. Mutual correlation values for the test sets were 0.85 and 0.84 for the first and second study periods at the Bonab station, respectively, for the SVM model test set. For the Khormazard station, these values were 0.79 and 0.75, respectively, for the RF model test set. Other notable findings of this research include the analysis of the time series data for rainfall and runoff over 43 years. Graphs obtained for both stations, along with the Mann-Kendall statistic for precipitation and flow parameters, revealed no discernible trend in precipitation during the two study periods. Instead, precipitation in these areas displayed fluctuating patterns However, the analysis of the time series and statistical values for the discharge of Sofichai and Mahpari chai rivers at the Bonab and Khormazard stations showed different results. In the Bonab station, the discharge exhibited fluctuations, with an increase observed in the second period. Conversely, at the Khormazard station, the discharge trend was downward in both study periods. The volume of Mahpari chai River outflow notably decreased in recent years, as evidenced by the Mann-Kendall statistic showing a decreasing trend.

In the present study, in order to evaluate the effect of stress periods on wheat yield gap in rainfed lands of Bostanabad, Tabriz, Sarab, Maragheh and Harris regions located in the east of Urmia Lake basin, in periods of 11 to 20 years, the water requirement satisfaction index has been used. The linear production function, which was provided by FAO, was also implemented to estimate the potential yield. According to the obtained results, in terms of water supply percentage of rainfed wheat or evapotranspiration ratio in rainfed to standard conditions, Harris with 42% has the best situation and Sarab with 23% has the worst situation. On average, the actual yield of rainfed wheat in the east of Urmia Lake basin is 976 kg/ha. The potential yield was estimated at 4216 kg/ha, indicating a significant gap between potential and actual yield in the study area. The highest regional yield coefficient equal to 0.41 was related to Harris and the lowest value was equal to 0.21 related to the Maragheh region, respectively. The results obtained from the water requirement satisfaction index indicate the existence of suitable conditions for the production of rainfed wheat in the region. However, the actual yield in the east of Lake Urmia basin is on average about 27.2% of the potential yield. This shows that the condition of rainfed lands in the east of Lake Urmia basin is not favorable in terms of the development of the production system and it is necessary to reduce the yield gap. Additionally, the evaluation of the results showed that the correlation of the water requirement satisfaction index with the regional yield coefficient is higher compared to the actual yield and this index can be used to evaluate the yield gap.

Evapotranspiration (ET), a major component of the hydrologic cycle, is important in water resources management and irrigation scheduling. Nowadays, due to the lack of the lysimetric data in weather stations, the ET values calculated by the standard FAO Penman-Monteith model ( ) are used as benchmark values of grass reference crop. Also, the Penman-Kimberly model is widely applied for computing the alfalfa-reference crop ET ( ). In the present study, the meteorological data from 6 weather stations located in the Sistan-Va-Baluchestan Province covering a period of 10 years were used to calculate the and values. Then, the to  ratios were computed for all six stations during the studied period. The Penman-Kimberly model at Mirjavah station had the worst result compared to other stations. The NS coefficient values for this station are the lowest (0.07) and the SI and RMSE values for this station are 0.43 and 2.48, respectively, which is the highest value among the study stations. Finally, the contributions of the energy balance and aerodynamic components on the final ET values were determined using the Penman-Kimberly model, which showed the important influence of both components on the ET process. Consequently, the use of radiation-based models e.g. Priestly-Taylor model in these stations should be carried out by special care.

Few studies have been done regarding the role of the raindrop in the hydrodinamic mechanism of soil erosion. In this study, rainfall simulation experiments were conducted to evaluate the role of raindrop in runoff discharge, sediment concentration and hydraulic properties of flow under four slope gradients (5, 10, 15 and 20%) in a clay soil using a 90 mm.h-1 rainfall intensity to reach the steady state flow. Soil sample was packed into the erosion flume with 0.3m× 0.4m × 4 m in dimensions and tested under two soil surface conditions:  one with raindrop impact and one without raindrop impact. The results showed that runoff discharge, sediment concentration, flow depth, shear stress, stream power, Reynolds number and runoff velocity under without raindrop impact condition were significantly lower than those in the condition  with the raindrop impact with a factor of 0.62 to 3.54, 0.08 to 11.83, 0.91 to 0.96, 0.26 to 3.25, 0.52 to 4.45, and 0.36 to 3.27, 0.23 to 0.79 times, respectively; on the other hand, the Darcy Wysbach, Chezy and Manning coefficients were increased significantly under without raindrop impact (P<0.01). Flow velocity was the key hydraulic parameter strongly affecting the hydraulic properties. These findings indicated the importance of raindrop impact in the detachment rate of soil particles through the change of the hydraulic characteristics. This study also revealed the key role of raindrop impact on the runoff hydraulic characteristics, as well as particle detachments rate in rills. Information about the role of raindrop impact is a substantial step in modeling the rill erosion. Therefore, elimination of raindrops impact, especially in the steep slopes, with the conservation of natural vegetation cover can sufficiently prevent runoff production as well as the particle detachment rate.

Rills are often the major area and source of runoff and sediment in hillslopes. The hydraulic characteristics of flow in the rills can be affected by type and amount of sediment. Knowledge on the flow hydraulic characteristics is essential to find the major mechanism of rill development. The objective of this study was to evaluate the effects of type and amount of sediment on the rill hydraulic characteristics. Laboratory experiments were carried out in a flume with 4m× 0.4 m×0.3m dimensions located at 9% slope steepness under 90 mm h-1 rainfall intensity which was applied on three soil textures of loam, loamy sand and sandy clay loam with four replications. Reynolds number, Froude number, flow depth, Darcy–Weisbach friction coefficient, shear stress and stream power were determined during the rill erosion process. The results showed that Reynolds number and Darcy–Weisbach friction coefficient increased as the sediment load increased in the rills. Froude number decreased as sediment load increased which was attributed with decreasing flow velocity. Shear stress and stream power and flow depth increased with increasing the eroded sediment. Flow depth increased with incease of sediment load, varying from 0.041 to 1mm in loam soil, 0.07 to 1.3 mm in loamy sand soil and 0.06 to 0.7 mm in sandy clay loam soil. Coefficient of determination between sediment load and all hydraulic characteristics were higher than 90% for loam and loamy sand and higher than 60% for sandy clay loam. This study revealed that significant interaction was between sediment load and hydraulic characteristics of flow in the rills.

Extraordinary withdrawal of groundwater resources has caused severe damages to aquifers, and the country's plains are facing subsidence due to this situation. In this regard, following the encouragement of the government by offering financial supports, many of farmers have volunteered to run pressurized irrigation systems. Therefore, gathering proper quantitative and qualitative data of water resources is necessary for sustainable agriculture. In this study, water quality of 103 agricultural wells in Sarab plain, which owners are applying for the operation of pressurized irrigation system, was investigated. To determine the water quality conditions of these wells, the magnesium absorption ratio (MAR), permeability index (PI), salinity potential (PS), Kelley's Ratio (KR), sodium absorption ratio (SAR) and soluble sodium percentage (SSP) indices were evaluated along with some other chemical parameters in the studied plain. The EC, SAR, MAR, PS and chloride indices indicated a decline in water quality from east to west and from south to north of plain. Approximately, water in 50% of wells were in the C3S1 class and the average KR of all wells were in a suitable condition with the value of 0.46. Except for one case with the value of 15.52, the remaining wells water had non-alkaline water with the SAR values less than 15. Only 17% of wells water had SSP values over 40% and they were not suitable based on this indicator. Most of the samples were located below the standard limit of 50% for MAR. Finally, in this study condition the application of sprinkler and tape irrigation systemsfor 90% of wells is recommended and for 10% of the rest of wells gated the pipes irrigation system may be adapted.

In this research، five-drip irrigation systems were selected in Sattarkhan Channels Network and technical performances of them were evaluated. In each system one manifold was randomly selected and four located lateral pipes، at the beginning، one-third، two thirds and end of each manifold were selected. By considering the same proportionality، four apple trees were identified a long each lateral pipe. Emitter discharges of two drippers at each of these trees، pressure at the beginning and at the end of the lateral pipes were measured in the all selected manifold pipes. Also، the minimum lateral inlet pressure at each manifold was identified by measuring the flow pressure of the all lateral inlets. Results showed that the water Emission Uniformities of the systems (EUs) were within the range of 48. 1 to 82. 8 percent and the systems performances based on this index were classified poor to good. Potential application efficiencies of the low quarter of the systems (PELQs) were varied from 43. 3 to 74. 5 percent. So that، only the irrigation system at one of the farms had relatively good performance and the systems'' performances in the other farms were poor. Application efficiencies of low quarter of the systems (AELQs) were in the range of 52. 7 to 80 percent. Based on AELQs index، only one system had good performance while the performances of the others were poor. In general، the reasons for the low system performances were pointed as inappropriate distribution of pressure، high-pressure difference in the systems، the unsuitable irrigation water depth، low skills of irrigators and poor operation management of the systems.

Optimal crop water requirement is needed for precise irrigation scheduling. Prediction of crop water requirements is a basic factor to achieve this goal. In this study, maize potential evapotranspiration (ETp) was prediced by maize simulation model (MSM). Then, it was evaluated and validated using experimental field data obtained in Agricultural Research Station of Shiraz University (Bajghah, Fars province) during 2003 and 2004. Comparison of measured volumetric soil water content with predicted values by MSM model in 2003 and 2004 indicated that this subroutine (prediction of maize evapotranspiration) did not need modification. Also, daily potential evapotranspiration of maize was estimated by using Penman-Monteith equation considering single and dual crop coefficients. Comparison between the results of predicted ETp by MSM model, calculated ETp by Penman-Monteith, and measured irrigation water and soil water content indicated that the prediction of ETp by MSM model was satisfactory. Model prediction of seasonal ETp, potential transpiration (Tp) and soil evaporation (E) were 831, 536 and 329 mm, respectively, in 2003, and 832, 518 and 314 mm, respectively, in 2004. The values of ETp, Tp and E calculated by Penman-Monteith method using dual crop coefficients were 693, 489 and 205 mm, respectively, in 2003, and 700, 487 and 213, respectively in 2004. Maximum rate of predicted potential ETp, Tp and E were 11.1, 8.2 and 5.1 mm d-1, respectively in 2003 and 13.0, 9.0 and 4.0 mm d-1, respectively in 2004. The values of calculated seasonal ETp by Penman-Monteith method using single crop coefficient were 615 and 632 mm in 2003, and 2004, respectively. Comparison between the results of predicted ETp by MSM model, calculated ETp by Penman-Monteith equation with single and dual crop coefficients (FAO-56) and measured values of irrigation water and soil water contents of root depth indicated that FAO-56 methods underestimated the ETp.

Agricultural investigations use computer models for simulation of crop growth and field water management. By using these models, the effects of plant growth parameters on crop yields are simulated, hence, the experimental costs are reduced. In this paper, the model of MSM (Maize Simulation Model) was calibrated and validated for the prediction of maize forage production at Agricultural College, Shiraz University in 1382 and 1383 by using maize forage yield under furrow irrigation with four irrigation and three nitrogen treatments. Irrigation treatments were I4, I3, I2, and I1, with the depth of water 20% greater than, equal to, 20% and 40% less than potential crop water requirements, respectively. Nitrogen treatments were N3, N2, and N1, with the application of N as urea equal to 300, 150, and 0 kg N ha-1, respectively. After calibration and validation of MSM, it was used to estimate suitable planting dates, forage yield and net requirement of water discharge for planting at different dates. The results indicated that the net requirement of water discharge was reduced by gradual planting at different planting dates. By considering different planting dates for maize, from Ordibehest 20th to Tir 10th, the planting area might be increased 17.9%, compared with single planting date on Ordibehesht 30th under a given farm water discharge and full irrigation.