tree model

The constant need to increase agricultural production, along with more and more frequent drought events in the country, requires a more accurate assessment of irrigation needs and thus a more accurate estimate of actual evapotranspiration. Prediction of water consumption over agricultural areas is important for agricultural water resources planning, management, and regulation. It leads to the establishment of a sustainable water balance, mitigates the impacts of water scarcity, as well as prevents the overusing and wasting of precious water resources. As evapotranspiration is a major consumptive use of irrigation water and rainwater on agricultural lands, improvements of water use efficiency and sustainable water management in agriculture must be based on the accurate estimation of ET. Irrigated agriculture is expected to produce more crops with less water consumption in the future. Therefore, accurate forecasting of water demand along with sustainable management and more efficient methods to meet the growing demand under scarce water resources is necessary. The models used to predict evapotranspiration should be used in different regions with different climates to evaluate their performance. Therefore, in this research, tree models and Hargreaves were used in Yazd and West Azerbaijan provinces, which have different climates, in order to evaluate the performance of the models used.

In recent years, water management issues have been addressed using models derived from artificial intelligence research. In recent years, water management issues have been addressed using models obtained from multiple types of research. The use of combined models has made significant progress in recent years. combined models are able to perform processing in a short period of time and at the same time with high accuracy. Using these models, the main challenging aspects are represented by the selection of the best possible algorithm, the selection of suitable representative variables and the availability of suitable data sets. Therefore, in this study, the ability of tree models (M5P and RF) with Hargreaves model (Hs) in estimating daily evapotranspiration in Urmia and Yazd stations during the period of 2000-2021. The noteworthy point is that in the combined tree-Hargreaves model, the used tree models were used as input to the Hargreaves model. The combined model has been used for the first time in this research and the use of this model can predict daily evapotranspiration as well as possible.

The results of the model are performed using 5 evaluation criteria of Coefficient of determination, Root mean square error, Nash-Sutcliffe coefficient, and Wilmot’s index of agreement. In all the used models, the best scenario was the model whose input included parameters of minimum temperature, maximum temperature, relative humidity, wind speed, and sunshine hours. Comparison and evaluation of standalone tree models showed that in the Urmia station two models RF-5 and M5P-5 had less error (0.4 and 0.38-mm day-1, respectively) than other standalone models. Similarly, in the Yazd station, RF-5 and M5P-5 models have higher accuracy (0.36 and 0.35 mm day-1(, respectively) than other standalone models. For combined models, the obtained results showed that the fifth scenario of the M5P-Hs model provided the best performance in Urmia and Yazd stations with the lowest error (0.33 and 0.24 mm day-1) respectively. It was also concluded that the fifth scenario of the RF-Hs model in Urmia and Yazd stations had a lower error (0.36 and 0.26 mm day-1) than other models, respectively. Finally, tree models have increased the accuracy of the Hargreaves model in this research.

Finally, the RF, M5P, RF-Hs and M5P-Hs models were able to predict daily evapotranspiration values in the shortest time and with the highest accuracy. However, the results showed that the lower the model inputs, the weaker the model prediction. The results of this research showed that the combination of tree models with Hargreaves model is able to predict daily evapotranspiration values with high accuracy compared to individual models. The results of this research showed that the wind speed parameter is one of the most important meteorological parameters needed in estimating daily evapotranspiration, so adding this parameter results in the highest accuracy in all models. Also, due to the important role of wind speed in predicting daily evapotranspiration values and the unavailability of the maximum wind speed parameter in this research, it is recommended to use the maximum wind speed parameter as one of the model inputs for further studies.

Vortex vibrations in structures has been considered in some of engineering problems such as vibration at exchanger tubes and bridge and hydraulic structures designing. Accordingly, evaluation the maximum relative amplitude of transverse waves developed by obstacle, attracts a lot of attention. Reynolds number, Froude number, wave mode, ratio of longitudinal and transverse distances of obstacles to cylinder diameter and arrangement of cylinders used as input variables to M5 model. Ratio of wave amplitude to flow depth considered as an output variable. After training tree model, two different structures proposed for inline and staggered arrangement. There are four and five equations for inline and staggered arrangements proposed by tree model, respectively. In this study, Reynolds number that ignored in existing formula, used as a division variable at inline arrangement and performed important role in proposed equations. Root mean square error in tree model was 0.23 which in compare with other models, has been reduced 46 to 50 percent. In addition, accuracy of tree model estimated 81 percent which about 18 to 38 percent more than other equations. Discrepancy ratio of tree model oscillates between 0.52 and 0.66 which shows the performance of this model in wave maximum relative amplitude estimation.

Pan Evaporation data used to estimate crop water requirements, but in some cases due to lack of accurate data on measurements or defective equipments failure and lost data as referred in missing data. Since data integration is important for irrigation planning, it is necessary to correct the statistical errors. Many methods were used for finding missing data, In the meantime, neural network and tree models have high degree of accuracy, however these models were not compared and evaluated. The aim of this study is to compare the model tree and neural network for reconstructing missing daily evaporation data for four stations in Khuzestan province.
In this study, the required data from four stations include Aghajari, Bandar Mahshahr, Izeh and Bostan located in Khuzestan province were collected. On the basis of the Koppen climate classification, the climate of these stations is arid. The data related to the years 1997 to 2008 that include daily values of pan evaporation, wind speed, maximum and minimum air temperature, relative humidity, sunshine and the extraterrestrial radiation. The data were divided to two four-year period (2005 to 2008) and 12 years (1997 to 2008) and at any period after intentional removal of 5%, 10% and 20% of the measured data, their values were estimated with the use of tree and neural network models. The results of the model were compared using Statistical indices.
In tree model coefficient of determination for four years period were: 85%, 75% and 85% and for 12 years period were: 90%, 83% and 84% respectively. In neural network model coefficient of determination for 4years period were: 85%, 75% and 85% and for 12years period were: 90%, 82% and 85% respectively. A higher coefficient value for 12 years period showed that models are more accurate to estimate missing data for longer term statistical data. By increasing missing data from 5% to 20%, accuracy of models was diminished. This research also indicated that both models have similar accuracy in the estimation of missing data.
According to the results of this study, when more than 10 years of data are available, both neural network and tree models will have relatively good results. Also, when the number of missing data are less or missed in shorter periods, estimated values will be closer to the actual values. In order to improve and complete results, it is suggested that statistical estimates of missing data for different periods, such as 8 or 15 years are repeated and the best period to determine for best practice models.