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سراج ابراهیمی, رضا, اسلامیان, سعید, زارعیان, محمد جواد. (1401). پیش‌بینی اثرات تغییر اقلیم بر منابع آب زیرزمینی با استفاده از روش‌های هوش مصنوعی (مطالعه موردی: دشت تالش). , 12(3), 561-579. doi: 10.22059/jwim.2022.340171.975
رضا سراج ابراهیمی; سعید اسلامیان; محمد جواد زارعیان. "پیش‌بینی اثرات تغییر اقلیم بر منابع آب زیرزمینی با استفاده از روش‌های هوش مصنوعی (مطالعه موردی: دشت تالش)". , 12, 3, 1401, 561-579. doi: 10.22059/jwim.2022.340171.975
سراج ابراهیمی, رضا, اسلامیان, سعید, زارعیان, محمد جواد. (1401). 'پیش‌بینی اثرات تغییر اقلیم بر منابع آب زیرزمینی با استفاده از روش‌های هوش مصنوعی (مطالعه موردی: دشت تالش)', , 12(3), pp. 561-579. doi: 10.22059/jwim.2022.340171.975
سراج ابراهیمی, رضا, اسلامیان, سعید, زارعیان, محمد جواد. پیش‌بینی اثرات تغییر اقلیم بر منابع آب زیرزمینی با استفاده از روش‌های هوش مصنوعی (مطالعه موردی: دشت تالش). , 1401; 12(3): 561-579. doi: 10.22059/jwim.2022.340171.975

پیش‌بینی اثرات تغییر اقلیم بر منابع آب زیرزمینی با استفاده از روش‌های هوش مصنوعی (مطالعه موردی: دشت تالش)

مدیریت آب و آبیاری
دوره 12، شماره 3، شهریور 1401، صفحه 561-579    اصل مقاله (4.57 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22059/jwim.2022.340171.975
نویسندگان
رضا سراج ابراهیمی1؛ سعید اسلامیان* 2؛ محمد جواد زارعیان3
1گروه مهندسی عمران، واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران.
2گروه مهندسی آب، دانشکده کشاورزی، دانشگاه صنعتی اصفهان، اصفهان، ایران.
3پژوهشکده مطالعات و تحقیقات منابع آب، مؤسسه تحقیقات آب، تهران، ایران.
چکیده
با توجه به افزایش گازهای گلخانه‌ای و به دنبال آن بحران‌های آبی و اقلیمی متعدد، پیش‌بینی دقیق تغییرات سطح آب زیرزمینی در مدیریت منابع آب مهم و ضروری است. لذا در این مطالعه به بررسی تغییرات اقلیمی دشت تالش تحت سناریوهای RCP با استفاده از Lars-WG و منابع آبی آن از مدل‌های SVR و ANN با استفاده از پارامترهای پمپاژ از آبخوان، تبخیروتعرق پتانسیل، دمای کمینه و بیشینه، بارش طی بازه زمانی (1410-1400) پرداخته شد. نتایج تغییرات میانگین دمای کمینه و بیشنه تحت سناریوهای RCP حاکی از افزایش دما به میزان 9/0 و 69/0 درجه‌سانتی‌گراد می‌باشد. همچنین بررسی دقت مدل‌های SVR و ANN نشان می-دهد که AUC در مرحله آموزش و آزمایش در مدل ANN حداکثر مقادیر AUC برابر با 876/0 و 769/0 محاسبه گردید در حالی که مدل SVR حداکثر مقادیر آن برابر 867/0 و 819/0 برآورد گردید، لذا SVR دقت پیش‌بینی بهتری دارد. همچنین در طی بازه زمانی (1399-1384) سطح آب زیرزمینی به میزان 10 سانتی‌متر و در مدل SVR و ANN به ترتیب به میزان 9 و 6 سانتی‌متر به‌طور متوسط طی بازه زمانی ذکر شده کاهش سطح ایستابی داشته است. همچنین در بازه زمانی (1410-1400) در مدل‌های SVR و ANN به ترتیب به میزان 18، 20 و 21 سانتی‌متر، 20، 21 و 23 سانتی‌متر به ترتیب تحت سناریوهای RCP2.6، RCP4.5 و RCP8.5 کاهش سطح آب زیرزمینی رخ داده است. از این‌رو پیشنهاد می‌گردد در دشت تالش توجه به الگوی کشت متناسب با منابع آبی در نقاط مختلف دشت در اولویت برنامه‌ریزان بخش کشاورزی قرار گیرد.
کلیدواژه‌ها
ب زیرزمینی؛ تغییر اقلیم؛ رگرسیون بردار پشتیبان؛ شبکه عصبی مصنوعی
عنوان مقاله [English]
Predicting the effects of climate change on groundwater resources using artificial intelligence methods (Case study: Talesh plain)
نویسندگان [English]
Reza Seraj Ebrahimi1؛ Saeid Eslamian2؛ Mohammad Javad Zareian3
1Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
2Department of Water Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, Iran.
3Department of Water Resources Study and Research, Water Research Institute, Tehran, Iran.
چکیده [English]
Due to the increase in greenhouse gases and numerous water and climate crises, accurate prediction of  the changes groundwater levels is very important and vital in the  water resources management. Therefore, in this paper,the  climate changes of Talesh plain is studied under RCP scenarios using Lars-WG and its water sources from SVR and ANN models. Also,aquifer pumping parameters, evapotranspiration potential, minimum and maximum temperature and  precipitation are used from (2021-2030). The results of the mean minimum and maximum temperature changes under RCP scenarios indicate the temperature increase by 0.9 and 0.69 °C. Also,studying  the accuracy of SVR and ANN models shows that the AUC in the training and testing phase in the ANN model, the maximum AUC values ​​were calculated as 0.876 and 0.769, while the SVR model, the maximum values ​​were equal to 0.867 and 0.819.Thus SVR has better predictive accuracy.In addition to that  during the time period (2005-2019) the groundwater level has decreased by 10 cm and in the SVR and ANN models by an average nine and six cm respectively more ever during in the time period(2021-2030) ground water levels have decreased in by 18, 20 and 21 cm, 20, 21 and 23 cm under the scenarios RCP2.6, RCP4.5 and RCP8 5 in SVR and ANN models,respectively.Therefor it is suggested that in Talesh plain considering the cultivation pattern appropriate to water resources in different parts of the plain should be the  priority for agricultural planners.
کلیدواژه‌ها [English]
Artificial Neural Network, Climate change, Groundwater, SVR
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