tooraj honar

Evapotranspiration is one of the most important components of the hydrology cycle for planning irrigation systems and assessing the impacts of climate change hydrology and correct determination is important for many studies such as hydrological balance of water, design of irrigation irrigation networks, simulation of crop yields, design, optimization of water resources, nonlinearity, inherent uncertainty, and the need for diverse climatic information in estimating evapotranspiration have been the reasons why researchers have used artificial intelligence-based approaches. In this study, to estimate accurately the daily reference evapotranspiration between 2009-2018 in Zabol city, north of Sistan and Baluchestan province,  first was used a standard FAO-Penman-Montith method and Zabol synoptic station meteorological data- the ETo reference transpiration is calculated and then presented by various scenarios of meteorological parameters including: maximum, minimum and mean temperature, maximum, minimum and mean humidity, precipitation, sunshine, wind speed and evaporation as inputs for deep learning models, Random forest and generalized linear model were attempted on a daily time scale More accurately. In estimating daily evapotranspiration in these models, 25 scenarios were selected from meteorological data combination and FAO-Penman-Monteith method was used to evaluate the models. Among the investigated scenarios, the M5 scenario (maximum, minimum and mean temperature, maximum, minimum and mean humidity, wind speed, pan evaporation) for deep learning model with minimum error (0.517) and highest correlation coefficient (0.517). 0.996 had the best performance among the above models. The deep learning model showed more accuracy and stability than other models. Hence, this study is recommended a deep learning model for estimating reference plant evapotranspiration in Sistan plain.

In this study, the effect of supplemental irrigation timing (treatments included no supplemental irrigation; supplemental irrigation in middle of March; supplemental irrigation in early May; supplemental irrigation in early August; and two supplemental irrigation events in middle of March and early May) and potassium (K) application (treatments included no-fertilizer; 150 and 250 g fertilizer per tree applied by fertigation method and no-fertilizer; 1.5 and 3.0 kg fertilizer per 1000 L water used by foliar application) on yield and yield quality of rain-fed fig trees were studied at the Estahban fig orchards in Fars province, Iran, through a three-year experiment. Results showed that supplemental irrigation for fig resulted in an increase in yield and fruit size, and improved the fruit peel color. However, it decreased total soluble solid of fruit. For one irrigation during the growing season, irrigation in late winter (March) with 1250 L of applied water per tree was better than irrigation in spring (May) with a similar amount applied water which was used as the supplemental irrigation of rain-fed fig trees under severe drought conditions. Two irrigation in March and May with 1500 L and 1250 L of applied water per tree for each event for the first year and two subsequent years, respectively, was more effective on the yield and fruit quality than single irrigation due to higher applied irrigation water. Using 150 g K per tree by fertigation method or 3.0 kg/1000L foliar K fertilizer resulted in significant effect on the yield and yield quality of fig and improved them.

 Iran is the fourth world producer of figs with an average of 75,833 tons  from 1993 to 2013 (FAO, 2016). Estahban area provides about 90% of dried fig in Iran (Jafari, Abdolahipour-Haghighi and Zare, 2012). The rainfed fig orchards in this area have been extremely affected by severe drought in recent years, leading to 10% to 80% reduction in fig trees and fruit in 2010, respectively (Jafari, Abdolahipour-Haghighi and Zare, 2012). For this reason, the tendency to apply supplemental irrigation in Estahban fig orchards has increased in previous years. However, the lack of information about the amount, timing, and application position of supplemental irrigation to achieve efficient use of water in this area makes it difficult to deal with this issue in the study area. The main objective of this study was, thus, to investigate the effect of different amounts and times of supplemental irrigation at different distances from tree trunk on soil water variation, quantity of fruits, and fig growers’ income under drought conditions.

Evapotranspiration as an important component of the hydrological cycle plays a very important role in the planning and management of water resources in dry and hyper dry areas. Accurate estimation of evapotranspiration requires a costly tool which can not be used anywhere. Hence, researchers are always looking for applied relationships and practices that are low-cost and accurate for the correct estimation of the value of this parameter. Several methods have been developed for the accurate estimation of evapotranspiration throughout the world. Among of these methods can be pointed out empirical equations, Artificial Neural Network, support vector machin, and tree models. Therefore, the purpose of this study was to investigate the accuracy and the capability of linear supporting vector machine models, the decision tree of the type of chiad and the random forest model in the estimation of reference evapotranspiration.The data used in this study include maximum temperature, minimum temperature, average temperature, maximum moisture content, minimum humidity, average humidity, precipitation, sunny hours, wind speed, and a shift from the meteorological station of Sistan Plain between2009-2018. In this study, using meteorological data and the FAO Penman-Monteith model, the values of evapotranspiration were calculated and then by providing different combination scenarios of the meteorological parameters as inputs of the studied models on a daily basis, an attempt was made to find a more accurate estimate of the refrence evapotranspiration as the output of the models. In this research, correlation coefficient (R) and Mean Absolute amount of Error (MAE) were used to compare different model. The results showed among the support model carriers, the random knife, the random forest model with M7 patterns has the highest accuracy with the correlation coefficient (R = 0.983) and the lowest mean error magnitude (MAE = 0.798). Therefore, this research recommends a random forest model for estimating evapotranspiration in the area of Sistan plain.

In the present study, using available records of daily rainfall of 22 years (1995-2016) of the Lamerd (Fars Province) weather station, frequencies and durations of rainy days were studied by using the Markov chain model. In this study, the months of May to October were disregarded due to the insignificant number of daily precipitations. The daily rainfall data were arranged based on the transition matrix of occurrence of dry and wet days, while the transition matrix was calculated based on the maximum likelihood method. In all studies done in Iran, in order to forecast precipitation by using the Markov chain, only the first order of the Markov chain was used which may not be in good agreement with data and resulted to incorrect results. But in this study, by using an accurate statistical method, the appropriate order of the Markov chain was diagnosed to be used. Matrices of stationary probability and the return periods of rainy days for 2 to 5-day precipitations were determined for the studied months in this research. The results showed that the probability of precipitation per day is 0.126, and the probability of absence of precipitation is 0.874.

In recent years, in order to solve problems were presented the way tasks in the agricultural sector. Among these, growth simulation models have been provided for water management, which it possible to reduce the difficulties in agricultural water. The main advantage of these models is that, they can optimize irrigation schedule economically. SWAP model is of the most widely used models in the world which has shown good results for variety of crops. So that, in this practice the performance of this model for simulating the growth of Canola was evaluated according to the data relating to two cultivation periods in Badjgah. The studied parameters consist of soil water content, evapotranspiration, dry matter, and seed yield. The results indicated that, in the calibration and evaluation phases for seed yield respectively, mean normalized error was 8.0 and 14.2. What is more, mean normalized error for simulation of dry matter was 3.9 and 10.8 in calibration and evaluation periods, respectively.

In order to study the effects of different irrigation regimes and plant density on yield component and protein concentration and crop water stress index (CWSI), under research field conditions a split plot arrangement was conducted in completely randomized block design during two years. The first factor of variables was the effects of 4 irrigation levels 120% ( 1), 100% (I2), 80% (I3) and 60% (I4) of Standard evapotranspiration and the second factor was the spacing of 5 cm (D1), 10 cm (D2) and 15 cm (D3) between plants within a row. Results indicate that the number of pods in each plant and the grains in each pod decreased when density increased, but the hundred-grains-weight and the height of bean plants increased. When irrigation increased, various increases were observed in the yield, the number of pods in each plant, the grains in each pod, the hundred-grains-weight and the height of bean plants, but the grain protein decreased. Protein concentration increased with more severe water deficit in the soil. The protein concentration directly correlated with total irrigation water during the growing season (TI) and the exponential equation P=33* e -4E-04TI can be used for protein concentration prediction. The effect of the irrigation water was significant and CWSI increased with increased soil water deficit. The effect of the density for CWSI was not significant. Grain yield (GY) directly correlated with CWSI and the exponential equation, GY=696.2(CWSI)-0.51 can be used for the prediction of grain yield. The CWSI value is useful for evaluating crop water stress in beans and thus it could be useful in timing the irrigation. Results of this research indicate that yield components such as height of bean plant, the number of pods in each plant and the number of grains in each pod were significantly different by applying the different irrigation strategies at the three different lengths of plant spacing within a row.

Groynes are constructed in arch or straight streams in order to protect river banks and reduce their erosion. Local scour at the nose of groynes is one of the most important issues in designing groynes that is created due to the contraction of the flow section and presence of strong vortexes. In this study, experiments were carried out on series of straight groynes located in a laboratory channel bend with different flow discharges and different angles under clear water scour condition. The maximum scour depths and sedimentation (deposition) area between them were measured and compared with each other. In order to assess the flow pattern, velocity measurement around the groynes was done with an Electromagnetic velocitymeter. Results showed that the maximum scour depth was around the normal groyne and for the three normal, repelling and attracting arrangements, the first groyne had the maximum scour depth while this depth was minimum at the last one. Also for the normal and repelling groynes, for a given ratio of velocity to the critical velocity, the sedimentation area between the first and second groynes was greater than that between the second and third groynes and for the attracting groynes, the inverse trend was confirmed. For various angles of groynes, while increasing the ratio of velocity to the critical velocity, the deposition between the groynes was reduced.