نوع مقاله : مقاله پژوهشی

نویسندگان

• غلامرضا علی پورمداب ¹
• احمد رضا قاسمی ²
• رسول میرعباسی نجف آبادی ²

¹ دانشجوی کارشناسی ارشد، گروه مهندسی آب دانشگاه شهرکرد

² دانشیار، گروه مهندسی آب، دانشکده کشاوری، دانشگاه شهرکرد، ایران

چکیده

به دلیل اهمیت داده‌های تبخیر از تشت در برآور نیاز آبی گیاهان و همچنین تخمین حجم هدررفت آب از مخازن آب سطحی و همچنین فقدان این داده‌ها در بسیاری از مناطق، بررسی و یافتن روش‌های جدید و کارآمد جهت برآورد میزان تبخیر از تشت، می‌تواند در مدیریت منابع آب موثر باشد. در این پژوهش از دو مدل ماشین‌ بردار پشتیبان (SVM) و مدل با حافظه مدت بلند کوتاه‌ (LSTM) و همچنین سه روش تجربی مایر، سازمان عمران آمریکا و ایوانف جهت برآورد تبخیر از تشت در استان کهکیلویه و بویراحمد استفاده شد. برای این منظور، داده‌های تبخیر واقعی در مقیاس روزانه برای 6 ایستگاه‌ دهدشت، امامزاده‌جعفر، لیکک، دوگنبدان، سی‌سخت و یاسوج و در دوره آماری 14 تا 39 ساله منتهی به سال 2023، از سازمان هواشناسی کشور تهیه و به‌عنوان متغیر وابسته در مدل‌ها مورد استفاده قرار گرفت. همچنین از 12 پارامتر هواشناسی شامل بارندگی، سرعت باد، دمای حداکثر و حداقل، ابرناکی، دمای خاک، حداکثر و حداقل رطوبت نسبی، میانگین دمای نقطه شبنم، فشار سطح دریا، تابش خورشیدی و میانگین فشار بخار واقعی نیز به‌عنوان متغیر مستقل استفاده گردید. نتایج نشان داد مقادیر خطای برآورد تبخیر در 6 ایستگاه مورد مطالعه برای مدل‌های تجربی بسیار بالا (حداقل مقدار خطا 27 درصد) و نتایج از نظر شاخص عملکرد مدل (ضریب نش) غیر قابل قبول می‌باشند. این امر ضرورت استفاده از مدل‌های دیگر در این استان را روشن می‌کند. نتایج ارزیابی دو مدل SVM و LSTM نشان داد، مدل SVM با خطای 16 درصد در مرحله آموزش و 18 درصد در مرحله آزمون، نتایج دقیقتری نسبت به مدل LSTM دارد. مقدار خطای برآورد تبخیر در این دو مرحله، در مدل LSTM به ترتیب برابر 23 و 24 درصد بدست آمد. در نهایت می‌توان نتیجه گرفت که، استفاده از مدل‌های قدرتمند مانند SVM که بر مبنای روابط ریاضی- آماری، قابلیت تشخیص و شبیه‌سازی رفتارهای پیچیده و غیرخطی متغیرهای اقلیمی را دارند، می‌تواند دقت تخمین تبخیر از تشت را در مناطق فاقد تشت تبخیر، افزایش دهد.

کلیدواژه‌ها

• تبخیر از تشت
• مدل‌سازی
• SVM
• LSTM

موضوعات

• تبخیر و تعرق

عنوان مقاله [English]

Estimation of daily pan evaporation in Kohgiluyeh and Boyer-Ahmad Province

نویسندگان [English]

• gholamreza alipoor ¹
• ahmadreza ghasemi ²
• Rasoul Mirabasi Najaf Abadi ²

¹ Master's student, Department of Water Engineering, Shahrekord University

² Associated Professor, Water Engineering,, Faculty Agriculture, Shahrekord University, Iran

چکیده [English]

Evaporation is a physical phenomenon by which water particles enter the atmosphere in the form of vapor from the surface of water or the surface of wet soil through receiving solar energy. Evaporation process is an essential part of the hydrological cycle and it takes place continuously in nature. The importance of evaporation estimation in water resources and agriculture studies is undeniable. The evaporation pan is used as an indicator to determine the evaporation from the water level of lakes and reservoirs all over the world due to the ease of interpreting its data. Evaporation prediction is one of the important variables in planning and managing water resources. Various models are used to predict evaporation, such as stochastic time series models, experimental models, data mining models. Due to the fact that evaporation is affected by many parameters that are non-linear, non-stationary and random and occurs in a dynamic and complex manner, the use of experimental methods to predict evaporation that is based only on weather information such as temperature, relative humidity, wind speed and solar radiation are used, the accuracy is not enough. So far, various methods have been presented to calculate and estimate evaporation, such as the water balance method and experimental methods. These methods have flaws such as the inability to generalize to other places and different climatic conditions. Therefore, researchers tried to find a way to reduce the error as much as possible. The use of machine learning models are among these methods.

Materials and Methods

In this research, two techniques of support vector machine and network with long short-term memory will be used to estimate the amount of evaporation from the pan in Kohgiluyeh and Boyer-Ahmad Province, and the results will be compared with the actual evaporation values recorded in evapotranspiration stations and some experimental methods of evaporation estimation. In order to model and estimate the amount of evaporation, daily data of parameters of pan evaporation, rainfall, wind speed, maximum and minimum temperature, cloudiness, soil temperature, maximum and minimum relative humidity, average dew point temperature, sea level pressure and actual vapor pressure has been prepared from the regional water organization for 6 stations including Yasouj, Imamzadeh Jafar, Dehdasht, Dogonbadan, Siskhet and Likk. The length of the statistical data period varies from 39 years in Dogonbadan to 14 years in Likk. In this research, two SVM and LSTM models, as well as three experimental methods of Mayer, US Civil Engineering Organization (USBR) and Ivanov were used to estimate evaporation from pans. To use the models to estimate evaporation, first the data were randomly divided into training and testing stage. 70% of the data were considered as training data and 30% of the data were considered as test data. Finally, in order to evaluate the models, the statistical indices of explanation coefficient (R2), root mean square error (RMSE) and mean absolute value of error (MAE), model efficiency index or Nash Sutcliffe coefficient (N.S) and residual mass index (CRM) were used.

Results and Discussion

The results of the SVM model show that the error values of daily pan evaporation estimation in the SVM model in the training stage vary between 1.6 and 1.2 mm per day. The efficiency coefficient also shows that the model is in a satisfactory condition in terms of estimation and forecasting ability in the Siskhet station, in a good condition in the Yasouj station, and in a very good condition in other stations. LSTM model has weaker results than SVM model. The error values in this model are higher than the SVM model and vary between 2.2 and 3 mm per day. Also, the results of the efficiency coefficient of the model (N.S) also show that this model has not provided acceptable results for estimating pan evaporation in the two stations of Sisakht and Yasouj. In part of the models test, the LSTM model also showed a weaker ability than the SVM model in most of the stations. The LSTM model has provided better results than the SVM model only in Imamzadeh Jafar station. The best result among all the stations and the two investigated models was obtained at the Dehdasht station with an error of 1.6 mm per day and a Nash coefficient of 0.87 for the SVM model. Comparison of the results of the models with 3 experimental methods of Mayer, Ivanov and USBR also showed that, except for Ivanov's method and at Doganbadan station, none of the experimental methods could simulate the daily evaporation values well.

Conclusion

In summary, the results of this research show that the SVM model has estimated the northern and central regions of the province with an error between 15 and 18 percent, while the LSTM model estimates the same regions with a greater error and about 22 to 26 percent. In the south of the province, the results is almost reversed and the SVM model has an error of about 19 to 21 percent and the LSTM model has an error of between 13 to 21 percent. In the east of the province, the amount of error in the estimation of pan evaporation in the LSTM model is also higher than in the SVM model. In total, the comparison of the results of two models and three experimental methods of estimating evaporation from pans in Kohgiluyeh and Boyer-Ahmad provinces showed that LSTM model is the best model for Imamzadeh Jafar station and SVM model is the best model for the other 5 investigated stations. None of the three experimental models, provided acceptable results for the province. The results of this research show that, despite the emergence of powerful models that are capable of detecting complex and non-linear behaviors of variables based on mathematical relationships that can well recognize and simulate the pattern of data variability, the use of experimental methods to estimate pans evaporation is not recommended.

کلیدواژه‌ها [English]

• Evaporation from pan
• LSTM
• Modeling
• SVM