توسعه مدل های تلفیقی بردار پشتیبان فراابتکاری و تجزیه محور در پیش بینی تبخیر از مخزن سد (مطالعه موردی: سد دز)
محورهای موضوعی : مدیریت منابع آب
رضا فرزاد
1
,
احمد شرافتی
2
,
فرشاد احمدی
3
,
سید عباس حسینی
4
1 - گروه عمران، واحد علوم و تحقيقات، دانشگاه آزاد اسلامي، تهران، ايران.
2 - گروه عمران، واحد علوم و تحقيقات، دانشگاه آزاد اسلامي، تهران، ايران.
3 - گروه هيدرولوژي و منابع آب، دانشگاه شهيد چمران اهواز، اهواز، ايران.
4 - گروه عمران، واحد علوم و تحقيقات، دانشگاه آزاد اسلامي، تهران، ايران.
کلید واژه: پيش بيني تبخير, سد دز , مدل فراابتکاري, تابع موجک, مدل SVR,
چکیده مقاله :
مقدمه و هدف پژوهش: تبخير از درياچه و مخازن سدها و همچنين خاک، يکي از مهم ترين فرآيند‌ها در مهندسي هيدرولوژيکي محسوب ميشود. پيشبيني دقيق تبخير يکي از فرآيند‌هاي ضروري براي مديريت صحيح و کارآمد مخازن، منابع آب، پايداري حوضه و فعاليت‌هاي کشاورزي مي‌باشد. تبخير بطور کلي تحت تاثير فرآيند آب و هوايي غيرخطي، غير ثابت و تصادفي است. چنين عواملي بهوضوح مانع از راه اندازي مدل‌هاي پيشبيني دقيق ميشود. تبخير، فرآيندي طبيعي است که بر پايه تامين انرژي و تبادل هوا انجام ميشود و طي اين فرآيند مولکول‌ها و اتم‌ها انرژي لازم براي خارج شدن از فاز سيال و وارد شدن به فاز گاز را پيدا مي‌کنند. از طرفي تغييرات اقليمي مي‌تواند بر پارامتر تبخير تاثيرگذار باشند به همين منظور پيشبيني تبخير از مخازن سدها اهميت وافري در بحث مديريت منابع آب دارد.
روش ها: در اين تحقيق با استفاده از مدل‌هاي SVR-ABC، Wavelet-SVR ، SVM با استفاده از 6 تابع موجک به پيشبيني تبخير از مخزن سد دز در استان خوزستان، ايران پرداخته شده است. سري داده‌ها از سال 1396-1350 به مدت 46 سال متعلق به ايستگاه هواشناسي سد دز در ايران مي‌باشند. همچنين در اين تحقيق از پارامترهاي هواشناسي بارش، حداکثر درجه حرارت، حداقل درجه حرارت، ميانگين درجه حرارت، حداکثر مطلق درجه حرارت و حداقل مطلق درجه حرارت استفاده شده است و بر اساس نتايج آنروپي شانون 5 پارامتر Tmax، Tmin، Tave، Tamax، Taminبه عنوان تاثيرگذارترين پارامترها جهت سناريو‌بندي در 5 گروه به ترتيب با 1پارامتر، 2 پارامتر، 3 پارامتر، 4 پارامتر و 5 پارامتر تقسيم‌بندي مي‌شوند که مجموعا 28 سناريو مورد مطالعه قرار مي‌گيرند.
يافته ها: نتايج مدلسازيها بر اساس شاخص‌هاي ارزيابي RMSE ، MAE و WI نشان مي‌دهد که عملکرد مدل فراابتکاري SVR-ABC با مقدار RMSE برابر 219/82 ،MAE برابر 977/53 و WI برابر 815/0 بهتر از مدل Wavelet-SVR با RMSE برابر 637/93، MAE برابر 360/69 و WI برابر 762/0 مي‌باشد. همچنين بر اساس نمودار ويولني مدل منفرد SVR با ورودي‌هاي تناوبي و غير تناوبي تبخير ماهانه را کمتر از مقادير مشاهداتي برآورد نموده و در نتيجه ميانگين داده‌ها در مقايسه با مقادير مشاهداتي کاهش يافته است.
نتيجه گيري: همچنين نتايج 6 تابع موجک استفاده شده در تحقيق نشان مي‌دهد که مدل تجزيه محور Wavelet-SVR با تابع موجک haar با سطح تجزيه 1 با شاخص‌هاي ارزيابي RMSE، MAE و WI به ترتيب 637/93 ميليمتر بر ماه، 360/69 ميليمتر در ماه و 762/0 مناسبترين نتيجه را در بين توابع موجک 6 گانه نشان مي‌دهد.
Introduction: Evaporation from lakes and reservoirs of dams as well as soil is one of the most important processes in hydrological engineering. One of the procedures required for proper and effective management of reservoirs, water resources, basin stability, and agricultural operations is the accurate prediction of evaporation. Weather phenomena that are non-stationary, unpredictable, and non-linear generally have an impact on evaporation. It is evident that these issues hinder the development of precise prediction models. Evaporation is a natural process based on energy supply and air exchange, during which molecules and atoms find the necessary energy to leave the liquid phase and enter the gas phase. On the other hand, climate changes can affect the evaporation parameter, so the prediction of evaporation from the reservoirs of dams is vital in the discussion of water resources management
Methods: This investigation uses the SVR-ABC, Wavelet-SVR, and SVM models with six wavelet functions to predict the amount of evaporation from the Dez dam reservoir in Khuzestan province, Iran. The data series from 1350–1396 for 46 years arises from the Dez dam meteorological station in Iran. The study also uses precipitation, maximum temperature, minimum temperature, average temperature, absolute maximum temperature, and absolute minimum temperature and Using the findings of Shannon's entropy, 5 parameters Tmax, Tmin, Tave, Tamax, Tamin are divided into 5 groups with 1 parameter, 2 parameters, 3 parameters, 4 parameters and 5 parameters respectively as the most effective parameters for scenario planning, which in total 28 scenarios are studied.
Findings: According to modeling results based on RMSE, MAE and WI evaluation indices show that the performance of SVR-ABC meta-heuristic model with RMSE=82.219, MAE=53.977 and WI=0.815 is better than Wavelet-SVR model with RMSE=93.637, MAE= 69.360 and WI=0.762. Additionally, based on the violin diagram, the single SVR model with periodic and non-periodic inputs estimated the monthly evaporation lower than the observed values, and as a result, the average data has decreased compared to the observed values.
Conclusion: Furthermore, according to the results of the six wavelet functions that were used in the study, the Wavelet-SVR axis decomposition model with the Haar wavelet function at decomposition level 1 and values of 93.637 mm/month, 69.360 mm/month, and 0.762, respectively, shows the most appropriate result among the six wavelet functions based on the RMSE, MAE, and WI evaluation indices.
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