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Background and Objectives Water shortage and the need for its optimal use in arid and semi-arid regions, including Iran, has led water officials and farmers to use modern irrigation systems, such as drip irrigation with the aim of making optimal use of water resources. Drip irrigation has been welcomed in most parts of the world due to its high efficiency and the possibility of irrigation in different environmental conditions. The most important reason for the superiority of drip irrigation over other irrigation methods is the controllable amount of water for each plant. Drip irrigation is a method in which water is poured out of the net at low pressure through an orifice or device called an emitter and dripped into the bottom of the plant. This irrigation system, like other methods, requires accurate knowledge of the parameters affecting it to achieve the desired efficiency. One of the most important parameters for the irrigation system is the distribution of moisture in the soil and in fact the shape of the moist bulb. Therefore, knowledge of how to distribute water in the soil is essential for the proper design and management of subsurface drip irrigation systems. Since testing is very difficult and time consuming to detect the shape of moisture distribution in the soil, the use of numerical and analytical simulation can be an effective and efficient way to design these systems. Methodology In order to determine the progress of the moisture front in drip irrigation, first the soil texture type and physical properties of the soil were determined. It should be noted that the emitter flow rate was measured and adjusted in volume at the beginning of the test to minimize the difference between the emitter flows along the three side tubes. Evaluation experiments were performed with three outflows of 2, 4 and 6 liters per hour. With the start of the system, the progress of the moisture front at different times was measured by digging a trench using a scale. Numerical simulation of moisture front progress was performed using HYDRUS model based on Richard equation and analytical simulation was performed using Moment Analysis. HYDRUS software was used to numerically simulate the progress of the moisture front. The simulation range was considered to be 100 cm by 100 cm on the two-dimensional plane. In these simulations, 3956 nodes are used to represent the entire simulation range and also, relevant equations were used to calculate the two-dimensional spatial Moment of the wetting pattern . Findings The simulations show that the initial volumetric moisture content is 0.11 and the saturation volumetric moisture content is 0.380 and the water dispersion rate increases over time on the x and z axes. With increasing flow, the maximum dispersion is in the x-axis, which occurs in flow of 6 liters per hour. The result for flow of 6 liters per hour based on the data used is slightly higher than the desired value. The reason why the value of M00 in the flow rate of 6 liters per hour is higher than expected, is that in the simulation flow rate of 6 liters per hour change in the size of the inlet diameter and the amount of flux changes the amount of water entering the soil and moistens a large volume of soil. Due to the different amount of moisture applied to the area at different times, the value of 𝑧𝑐, 𝜎𝑥 2, 𝜎𝑧 2 is different and has caused a change in the size of the oval in different flows. The increase in the size of the ovals indicates the high dispersion of water in that area. The results showed that the Moment analysis was able to express the position of the center of mass of water distributed in the soil with correlation coefficient of 0.986 in linear mode and 0.982 in logarithmic mode. By comparing the values of diameter and depth obtained from the HYDRUS and the drawn ovals, it can be concluded that both methods provide close results. The accuracy of the Moment analysis method in simulating different types of moisture patterns resulting from drip irrigation under different flows with the use of different volumes of water is similar to the HYDRUS model and therefore it is possible to use this feature to predict the pattern of moisture from a certain flow using a specific volume of water . Conclusion In this study, the accuracy of Moment analysis in simulating various moisture patterns resulting from drip irrigation under different flows with the use of different volumes of water was investigated and the possibility of using this feature to predict the pattern of moisture from a given flow using a specific volume of water checked. In order to investigate the Moment of the amount of water distributed in the soil by subsurface drip irrigation, simulation was performed by two-dimensional HYDRUS software for three discharges of 2, 4 and 6 liters per hour with an inlet water volume of 12 liters. Then, using the results of simulation of moisture distribution range by a programming language including MATLAB software, and by calculating the Moments, it was determined that the Moments are able to express the position of the center of mass of water distributed in the soil and how it is distributed relative to x and z axes. The increase in the size of the ovals indicates that more water is distributed in that area. Comparing the diameters and depths of the moisture front between the simulated HYDRUS model and the Moment analysis model, it was found that the Moment analysis is an efficient way to study the distribution of water moisture by drip irrigation and this method can be used as an alternative input to estimate parameters. [ABSTRACT FROM AUTHOR]

Background and Objectives Evapotranspiration is one of the main components of water balance in agriculture and is one of the effective and efficient factors for accurate irrigation planning and management. Direct measurement of evapotranspiration values is time consuming and costly. On the other hand, modeling such a complex process in which many parameters interact with each other is so difficult that it is not possible to simplify the issue without multiple assumptions. Therefore, accurate estimation of this parameter has always been considered by the researchers. In the other point of view, the FAO-56 method was used as the accurate and accepted method for calculating reference evapotranspiration. One of the weaknesses of this model is its dependence on various meteorological variables. Therefore, it is necessary to use methods which need low number of meteorological variables and estimate the reference evapotranspiration with high accuracy. Additionally, due to the use of many meteorological variables and the complexity of the calculations, it is difficult to use FAO-56 method in all regions. Therefore, in the recent years, many researchers implemented machine learning methods to estimate reference evapotranspiration. Most studies in the field of reference evapotranspiration estimation use experimental models that require all the effective reference evapotranspiration parameters to provide an acceptable estimate. Hence, the aim of the current study was to present a superior model from three machine learning models, including random forest (RF), gradient boosted tree (GBT) and generalized linear model (GLM) for estimating reference evapotranspiration in three synoptic stations located at arid, semi-arid and wet climates of Iran. To the best of our knowledge, the proposed GBT and GLM methods have not been used for estimating reference evapotranspiration in the mentioned stations. Methodology In this research, the FAO-56 method was used to estimate the reference evapotranspiration. Also, three machine learning methods including GBT, GLM and RF were implemented to estimate the amount of reference evapotranspiration. Daily parameters of some fundamental and effective meteorological variables on evapotranspiration during 21-years statistical period (2000-2020) were collected in three stations located at different climates including Yazd station (arid), Birjand station (semi-arid) and Sari station (wet). In order to investigate the possibility of using different combinations of meteorological parameters to estimate the reference evapotranspiration as accurately as possible, seven different combinations of meteorological parameters were defined. The accuracy of the utilized methods was evaluated using three criteria such as correlation coefficient, scattering index and Nash-Sutcliffe coefficient. Additionally, Taylor diagrams were implemented for evaluating the accuracy of the used methods. It should be noted that the Taylor diagram shows the three parameters of root mean square error, correlation coefficient and standard deviation simultaneously in one figure. Also, the most suitable combination of meteorological parameters that had good accuracy for estimating reference evapotranspiration, was suggested. Findings The results showed that in the best model at Birjand Station, and Yazd stations scenario number three by two meteorological variables of temperature and wind speed and in Sari station the scenario number two with temperature and relative humidity, the gradient boosted tree model was reinforced with Nash-Sutcliffe coefficient of 0.804, 0.826 and 0.733, with correlation coefficient of 0.997, 0.997 and 919 and scatter index of 0.249, 0.218 and 0.361 and the generalized linear model with Nash-Sutcliffe coefficient of 0.892, 0.931 and 0.869 correlation coefficient of 0.952, 0.966 and 0.933 and scatter index of 0.185, 0.137 and 0.252, respectively. Finally, the RF method with Nash-Sutcliffe coefficient of 0.954, 0.956 and 0.929, correlation coefficient of 0.978, 0.978 and 0.965 and scatter index of 0.121, 0.110 and 0.186 had good performance for estimating the reference evapotranspiration. On the other hand, in all methods, the scenario number seven using the meteorological parameters of temperature, relative humidity of sunny hours and wind speed in all three stations, presented the most accurate performance. Therefore, all three methods may be proposed as models with high degree of accuracy for estimating reference evapotranspiration. Conclusion Reference evapotranspiration is one of the main components of water balance in agriculture and is one of the effective and influential factors for accurate irrigation planning. Therefore, accurate estimation of this parameter has a significant role on reducing excessive water consumption. In this study, three data-driven models of RF, GBT and GLM were used in three stations of Yazd, Birjand and Sari stations. The obtained results indicated that the seventh scenario using all four meteorological parameters in all stations with the highest correlation coefficient, the lowest scatter index and the highest Nash-Sutcliffe coefficient provided most accurate estimates of the reference evapotranspiration and may be recommended for proper estimation of reference evapotranspiration. [ABSTRACT FROM AUTHOR]

Soil temperature (ST) stands as a pivotal parameter in the realm of water resources and irrigation. It serves as a guide for farmers, enabling them to determine optimal planting and fertilization timings. In the backdrop of regions like Iran, where water resources are scarce, a proficient and economical prediction model for ST, particularly at lower depths, becomes imperative. While recent models have demonstrated adeptness in predicting ST, in general, their error decreases with increasing depth, so that they had the lowest error at a depth of 100 cm. Addressing this gap, our study pioneers a novel hybrid model that excels in accurate daily ST prediction as it delves deeper. The models deployed encompass the multilayer perceptron (MLP) and an enhanced version, MLP coupled with the Sperm Swarm Optimization Algorithm (MLP-SSO). These models prognosticate daily ST across varying depths (5-100 cm), leveraging meteorological parameters such as air temperature, relative humidity, wind speed, sunshine hours, and precipitation. These parameters are anchored to the Ahvaz and Sabzevar synoptic stations in Iran, spanned over the period from 1997 to 2022. Evaluation of our research outcomes unveils that the root mean square error (RMSE) witnesses its most substantial reduction at a depth of 100 cm. For instance, at the Ahvaz station, the MLP-SSO model diminishes the RMSE value from 1.25 to 1.12 °C, in contrast to the MLP model. Similarly, at the Sabzevar station, the RMSE value drops from 1.78 to 1.49 °C using the coupled MLP-SSO model. These results robustly highlight the considerable enhancement brought about by the utilization of the MLP-SSO model, clearly surpassing the performance of the standalone MLP model. This emphasizes the potential and promise of the MLP-SSO model for future investigations, offering insights that can significantly advance the domain of soil temperature prediction and its implications for agricultural decision-making., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The purpose of the present study was to predict the pan evaporation values at four stations including Urmia, Makou, Mahabad, and Khoy, located in West Azerbaijan, Iran, using support vector regression (SVR), SVR coupled by fruit fly algorithm (SVR-FOA), and SVR coupled with firefly algorithm (SVR-FFA). Therefore, for the first time, this research has used the combined SVR-FOA to predict pan evaporation. For this purpose, meteorological parameters in the period of 1990-2020 were gathered and then using the Pearson's correlation coefficient, significant inputs for pan evaporation estimation were determined. The correlation evaluation of the parameters showed that the two parameters of wind speed and sunshine hours had the highest correlation with the pan evaporation values, and in addition, these parameters, as input to the models, improved the results and increased the accuracy of the models. The obtained results indicated that at Urmia station, SVR-FFA with the lowest error was the best model. The SVR-FOA also had better performance than the SVR model. Additionally, the result showed that SVR-FOA with the lowest errors had the best capability in pan evaporation estimation at other studied stations. Therefore, it was concluded that FOA with advantages such as simplicity, fewer parameters, easy adjustment, and less calculation can significantly increase the capability of independent SVR models. Hence, based on the overall results, SVR-FOA may be recommended as the most accurate method for pan evaporation estimation., (© 2023. The Author(s) under exclusive licence to International Society of Biometeorology.)

AquaCrop is one of the dynamic and user-friendly models for simulating different conditions governing plant growth in the field. But this model requires many input parameters such as plant information, soil, climate, groundwater, and management factors. In this research, to solve this problem and develop a model with fewer input data, artificial neural network (ANN), support vector regression (SVR), and combined support vector regression with firefly algorithm (SVR-FFA) were used. For this purpose, 440 scenarios were created in 2 farms located in Iran, and the values of yield and biomass obtained by the AquaCrop model were compared with intelligent models. Also, consumable seed and irrigation were considered as inputs of intelligent models. The 99WestW2 farm in Miandoab had a seed yield of 6.588 t/ha, and the WestW10 farm in Mahabad had a seed yield of 5.05 t/ha. The results of this research showed that for both 99WestW2 and WestW10 farms, the SVR-FFA3 model was able to have the lowest amount of error so that for the amount of grain yield, the error values ​​for the farms were 0.033 and 0.069 t/ha, respectively. The error value of biomass was obtained for farms as 0.057 and 0.066 t/ha respectively. After SVR-FFA model, SVR and ANN models also showed good performance due to proper accuracy and saving time. Finally, SVR-FFA, SVR, and ANN models were able to predict yield and biomass values in the shortest time and with the highest accuracy with only two inputs., (© 2023. The Author(s) under exclusive licence to International Society of Biometeorology.)

The barriers for the development of continuous monitoring of Suspended Sediment Concentration (SSC) in channels/rivers include costs and technological gaps but this paper shows that a solution is feasible by: (i) using readily available high-resolution images; (ii) transforming the images into image analytics to form a modelling dataset; and (iii) constructing predictive models by learning inherent correlation between observed SSC values and their image analytics. High-resolution images were taken of water containing a series of SSC values using an exploratory flume. Machine learning is processed by dividing the dataset into training and testing sets and the paper uses the following models: Generalized Linear Machine (GLM) and Distributed Random Forest (DRF). Results show that each model is capable of reliable predictions but the errors at higher SSC are not fully explained by modelling alone. Here we offer sufficient evidence for the feasibility of a continuous SSC monitoring capability in channels before the next phase of the study with the goal of producing practice guidelines.

Deposition of sediment is a vital economical and technical problem for design of sewers, urban drainage, irrigation channels and, in general, rigid boundary channels. In order to confine continuous sediment deposition, rigid boundary channels are designed based on self-cleansing criteria. Recently, instead of using a single velocity value for design of the self-cleansing channels, more hydraulic parameters such as sediment, fluid, flow and channel characteristics are being utilized. In this study, two techniques of neuro-fuzzy (NF) and gene expression programming (GEP) are implemented for particle Froude number (Fr p ) estimation of the non-deposition condition of sediment transport in rigid boundary channels. The models are established based on laboratory experimental data with wide ranges of sediment and pipe sizes. The developed models' performances have been compared with empirical equations based on two statistical factors comprising the root mean square error (RMSE) and the concordance coefficient (CC). Besides, Taylor diagrams are used to test the resemblance between measured and calculated values. The outcomes disclose that NF4, as the precise NF model, performs better than the best GEP model (GEP1) and regression equations. As a conclusion, the obtained results proved the suitable accuracy and applicability of the NF method in Fr p estimation.