فهرست مطالب fatemeh mikaeili

Evaporation is one of the main components of hydrological cycle and one of the effective climatic variables in arid areas such as Iran. Accurate estimate of evaporation rate plays an important role in sustainable development and optimal management of water resources. Evaporation is one of the essential processes, because it depends on meteorological variables such as solar radiation, air temperature, wind speed, relative humidity and atmospheric pressure, which are related to the topography and the climate of the region. Class A pan-evaporation is one of the standard and direct tools for measuring evaporation, which is used all over the world due to its ease of application in determining evaporation. However, in most stations accurate evaporation recording is not practical due to instrument limitations and maintenance problems. On the other hand, the temporal and spatial distribution of evaporation stations compared to meteorological stations is limited, so according to the problems mentioned, the use of meteorological variables in estimating the rate of evaporation from the pan will be useful. In different regions, the impact of different climatic factors on changes evaporation from the pan has not be fully understood, so the relatively accurate estimation and prediction of this phenomenon is an effective step in the relevant fields. In recent years, for estimating the amount of evaporation from the pan, a variety of intelligent systems and software calculations such as data mining methods have been developed.

In this study, meteorological data of Tabriz station in the period of 2003 to 2018 have been used to estimate the evaporation values from the class A pan. For this purpose, a simple correlation between meteorological variables and evaporation from class A pan was created and based on the result of this correlation, in the studied station the minimum temperature and relative humidity were inversely and the maximum and average temperature were directly affected by evaporation. Thus, ten combined scenarios were defined and modeling was performed using Support vector regression (SVR), Gaussian process regression (GPR), M5tree, Random forest (RF) and Linear regression (LR) methods. It should be noted that in this study, 70% of the data were selected for training and 30% for testing. Finally, the performance of each method in estimating evaporation values was evaluated using root mean squared error (RMSE), mean absolute error (MAE), Nash- Sutcliffe coefficient (NS) and Akaike information criterion (AIC).

The results showed that GPR10 method with RMSE = 1.90 mm/day, MAE = 1.48, NS = 0.81 and SVR10 method with RMSE = 1.92 mm/day, MAE = 1.51, NS = 0.8 had reasonable performance in estimating the values of daily evaporation from class A pan. The GPR method showed its higher capability to estimate daily evaporation values in all definition scenarios with the least error and the most accuracy. The SVR model with appropriate results was in the second place. The results of statistical parameters for random forest model were even weaker than the results of linear regression. In general, scenario number 10 with all meteorological variables and scenario number 1 with only the input minimum temperature variable had the best and weakest results among all defined scenarios, respectively. Scenarios 6 to 10 have more accuracy and less error and modeling structures with the least number of variables has the least accuracy. Also, wind speed and solar radiation variables were introduced as the most effective factors in estimating the evaporation rate from class A pan.

Evaporation is one of the important processes that cause the losses of half of precipitation in arid and semi- arid regions. Accordingly, knowledge of the amount of evaporation and its modeling as one of the most important hydrological variables in agricultural research and factors related to water and soil of great importance. So, accurate estimation of this phenomenon is essential. In this study, meteorological data from Tabriz station were utilized to assessment capability of machine learning methods. Evaporation values were estimated using five data mining methods including SVR, GPR, M5, RF and LR. Conclusively, the results of evaluation criteria indicated that GPR and SVR models using all variable of meteorological data performed more accurate than others. Finally, both of them are recommended to estimate the amount of evaporation from class A pan.

Reference evapotranspiration (ET0) is an important parameter in the interactions among soil, vegetation, atmosphere, surface energy and water. Direct measurement of evapotranspiration values is costly and time consuming. On the other hand, modeling this complex process in which many variables interact with each other is not feasible without considering multiple assumptions. In this regard, the FAO Penman-Monteith method is used in a wide range of climatic and environmental conditions. One of the weaknesses of FAO Penman-Monteith method is its dependence on various meteorological parameters. Therefore, it is necessary to implement methods with lower meteorological variables that can estimate ET0 with suitable accuracy. Thus, in the present study, an attempt was made to estimate ET0 with acceptable accuracy using machine learning models.

In the present study, daily meteorological parameters in the time period of 2000-2020 including maximum and minimum air temperature (Tmax, Tmin), mean temperature (T), wind speed (U2), average relative humidity (RH), maximum and minimum relative humidity (RHmax, RHmin) and sunshine hours (n) were obtained on a daily basis in three stations of East Azerbaijan province (Tabriz, Sarab, and Maragheh). Moreover, six scenarios were defined as input combinations. Then, using random forest (RF) method in two cases: Single random forest and using the genetic algorithm (GA) to optimize its effective parameters with considering the FAO Penman-Monteith model as a basis, the machine learning models were calibrated and validated for estimating ET0 values at studied stations. Furthermore, the performance of empirical equations in three groups based on temperature (Hargreaves, Blaney-Criddle and Romanenko), radiation (Irmak) and mass transfer (Meyer) were also investigated. It should be noted that 75% of the data were considered for calibration and 25% for the validation of machine learning methods. Finally, using the statistical criteria of correlation coefficient (CC), scattered index (SI) and Willmott’s Index of agreement (WI), a suitable machine learning method was introduced to estimate the reference evapotranspiration. Also, the most suitable combination of meteorological parameters for ET0 estimation was suggested.

The obtained results showed that in all studied stations, scenario 6 has the best performance, either in the case of single random forest (RF) or in the case of random forest optimized by genetic algorithm (GA-RF). Meteorological parameters of this scenario include minimum and maximum air temperature, minimum and maximum relative humidity, sunshine hours and wind speed. By optimizing the RF-6 parameters with the genetic algorithm at Tabriz station, the statistical criteria were improved (CC from 0.990 to 0.991, SI from 0.103 to 0.098). At Sarab station, the CC was increased from 0.980 to 0.982, the SI was decreased from 0.140 to 0.132 and the WI was increased from 0.989 to 0.990. At Maragheh station, CC was increased from 0.990 to 0.991, SI was decreased from 0.103 to 0.098 and WI remained unchanged at 0.995. In general, the decreasing trend of the scattered index for RF method from scenarios 1 to 6 can be understood by increasing the input parameters of the random forest method. Among the three groups of empirical methods based on air temperature, radiation and mass transfer for estimating ET0, the best performance was seen for the Blaney-Criddle method based on air temperature. In all studied stations, the GA-RF model showed better performance than the empirical methods. Also, GA-RF-5 with similar meteorological parameters with Blaney-Criddle method provided accurate ET0 estimations.

Determining the amount of daily evapotranspiration and consequently accurate estimation of water requirement of plants provide the basis for proper designing of irrigation systems by reducing installation costs and providing a suitable program for the use of water resources in the agriculture sector. So, in the present study, meteorological data from Tabriz, Sarab and Maragheh stations were used to evaluate the ability of machine learning methods including RF and GA-RF to estimate the values of reference evapotranspiration. The results showed the high accuracies of RF-6 and GA-RF-6 for all studied stations and Belany-criddel among the empirical models. In a more detailed look, the genetic algorithm had positive effects on increasing the model accuracies by reducing scattered index of GA-RF scenarios 1, 4, 5 and 6 in Tabriz and Maragheh stations as well as scenarios 1, 5 and 6 at Sarab station. Finally, it can be concluded that both RF and GA-RF models provided the most accurate estimates of daily reference evapotranspiration in the East Azerbaijan province.

In the present research, three data-driven models including M5P, REP tree, and random forest were used to estimate daily reference evapotranspiration. The abilities of these three models to estimate reference evapotranspiration were studied in single and combined modes. To this end, the daily meteorological data of five synoptic stations in Kerman province in the period from 2000 to 2020 were used. A combination of meteorological variables, using sensitivity analysis versus the reference evapotranspiration values ​​obtained from FAO-Penman-Monteith, was considered as input for each of the mentioned models. Finally, the accuracy of the mentioned models and empirical methods in estimating the evapotranspiration of the reference plant were compared using statistical indicators, and the superior model was selected. The results of validation data showed that the M5P model in the form of individually (RMSE = 0.083 and NS = 0.998 in Bam station) and the weighted averaging in the form of the ensemble (RMSE = 0.155 and NS = 0.994 in Bam and Sirjan stations) in all stations had better results for estimating evapotranspiration rates than other methods. In general, tree models, especially M5P, had better results in estimating daily evapotranspiration than empirical models.

The gradual increase in the world’s population requires continues increase in agricultural production. Climate change is one of the challenges of our society and frequent droughts affect large areas of the world, which requires more accurate management of water resources, both globally and in local catchments. Accurate estimation of components of the hydrological cycle is essential for proper irrigation scheduling. Most of the precipitation received by the earth is returned to the earth’s atmosphere by the process of evapotranspiration. On the other hand, because every process that takes place in the plant is dependent on water and one of the most common uses of water in the plant is evapotranspiration, so reducing amount of the water will have adverse effects on photosynthesis, crop production, product quality, etc. The complex and nonlinear relationship between the factors affecting the process of evapotranspiration, has caused researchers today to use new methods to accurately identify and predict this parameter. Reference evapotranspiration is a concept that uses the crop coefficient to obtain the actual water requirement. According to the FAO proposal, the FAO- Penman- Monteith equation was introduced as a benchmark method for calculating reference evapotranspiration values when measurements of this parameter are not available and there is no access to lysimetric data. One of the major advantages of this model is its physical basis and global validity, but this equation needs a large number of meteorological parameters that are often not available, instead empirical equations with low meteorological variables or modern methods such as artificial intelligence and machine learning methods can be used.

In this study, meteorological data related to two stations of Astara located in the humid region and Sirjan located in the arid region of Iran in the period of 2000-2020 were studied to predict the crop evapotranspiration values. As mentioned, the FAO- Penman- Monteith method has used as a standard method for calibration and evaluation of the other functional equations and machine learning methods. In this study, four types of empirical equations including Hargreaves –Samani, Makkink, Turk and Dalton were evaluated against the FAO- Penman- Monteith model. Also, modelling was performed using Support Vector Regression, Random forest and M5P Tree model. In this study, 70% of data were considered for training and 30% for testing. Finally, statistical parameters including root mean squared error (RMSE), correlation coefficient (R), scatter index (SI), Nash-Sutcliffe coefficient (NS) and Wilmot index (WI) were used to determine the performance of each mentioned methods in estimating reference evapotranspiration values.

Using different meteorological parameters in accurate prediction of evapotranspiration using 4 combined scenarios, calibration calculations were performed on 70% of data and validation calculations were performed on 30% of testing data implementing Weka software. The obtained results showed that the SVR3 and M5P3 models in Astara station with all meteorological parameters and having R= 0.993, RMSE= 0.201 and also, the SVR3 model in Sirjan station with R= 0.982, RMSE= 0.410 compared to the studied empirical methods provided better results in estimating the reference evapotranspiration and scenario 3 with all meteorological parameters was introduced as the top scenario. Among the empirical methods, Hargreaves- Samani was superior to some models only in Astara station. At Sirjan station, none of the empirical models performed better than the machine methods.

Accurate estimation of reference evapotranspiration in water resource management is essential. In this study, meteorological data from Astara and Sirjan stations were used to evaluate the ability of machine learning methods including SVR, RF and M5P to estimate the values of reference evapotranspiration and compared the results with empirical methods. The results showed that the high accuracy of the SVR3 model in both stations and in the next position M5P3 model for humid area. Empirical methods except Hargreaves- Samani had poor performance compared to data- driven models. Finally, the use of SVR and M5P methods in irrigation scheduling is recommended.

Dew point temperature is very important in various fields including meteorology for weather forecasts. Therefore, it is necessary to provide suitable models to accurately predict the value of this meteorological variable for the practical use of agricultural engineers and nearby stations where it is not possible to measure this temperature. In the present study, we investigated the ability of four data-driven models, including gradient reinforcement tree, M5P tree model, random forest, and random forest optimized with genetic algorithm, in estimating daily dew point temperature. For this purpose, the daily meteorological data of two stations in Ardabil and Parsabad were used in the period of 2014 to 2019. The used meteorological parameters include minimum, maximum, and average temperature, relative humidity, sunshine hour, and wind speed, which were considered input variables for each of the mentioned models in 10 different combinations. The comparison of the results obtained for both stations showed that the M5P-8 model with a root mean square error of 0.54°C and a Wilmot coefficient equal to 0.998 in the Ardabil station and the M5P-6 model with a root mean square error of 0.29°C and Wilmot coefficient equal to 1.00 was introduced as the best models in Parsabad station.

Due to the location of Iran in arid and semi-arid regions and the inhomogeneous distribution of precipitation, predicting the occurrence of precipitation is important, therefore, researchers are implementing novel methods to identify and predict this parameter accurately. Thus the purpose of the current study is to investigate the capabilities of Logistic Model Tree (LMT) in predicting the occurrence of daily precipitation at Parsabad station using 1 to 3-day meteorological data. For this purpose, meteorological data for 2004-2016 were collected, and three combined scenarios of meteorological parameters were considered for calibration and validation of the studied method. The results showed that the prediction accuracy of the best-case scenario using the data from 2 days ago was about 79%, however, with the data from 1 and 3 days ago, the daily precipitation was with 80% prediction accuracy. Finally, by investigating the evaluation criteria, scenario 1 with the input parameters of minimum, maximum and average relative humidity (%), temperature (oC), total sunshine hours, and wind speed (m/s) was determined as the most accurate scenario to predict daily precipitation.

Accurate estimation of reference evapotranspiration has great importance in irrigation scheduling. Moreover, the lack of availability of lysimetric data has led researchers to use indirect methods, including data-driven approaches. In the present study, the ability of Gaussian process regression (GPR), support vector regression (SVR) and random forest (RF) data-driven methods was investigated to estimate the evapotranspiration of the reference plant. For this purpose, meteorological data on average temperature, wind speed, relative humidity and sunny hours in the period 2013-18 were collected in nine northern stations of Iran including Astara, Bandar Anzali, Rasht, Ramsar, Nowshahr, Sari, Turkmen port, Gorgan, and Gonbad Kavous. Evapotranspiration calculated using FAO-Penman-Montith method was considered as the target output and four combined scenarios of meteorological parameters were considered to calibrate and validate the studied methods. The accuracy of the mentioned methods was compared using the statistical parameters of correlation coefficient, scatter index, and Wilmott’s coefficient. The results showed that GPR4 model with scatter index in the range of 0.132 to 0.179 in Astara, Bandar Anzali, Rasht, Ramsar, Nowshahr and Sari stations, SVR4 model with dispersion index of 0.116 to 0.120 in Turkmen and Gonbad Kavous stations and the Hargreaves-Samani method with a scatter index of 0.509 at Gorgan station had much more accurate estimates of the evapotranspiration of the reference plant.