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مدل‌سازی هیدرولیکی منابع آب با استفاده از تکنیکهای یادگیری | ||
| تحقیقات آب و خاک ایران | ||
| دوره 52، شماره 11، بهمن 1400، صفحه 2739-2750 اصل مقاله (1.59 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2021.330656.669078 | ||
| نویسندگان | ||
| مجتبی پورسعید* 1؛ امیرحسین پورسعید2؛ سعید شعبانلو3 | ||
| 1سازمان برنامه و بودجه، معاونت فنی | ||
| 2دانشجوی دکترای تخصصی مهندسی برق قدرت، گروه مهندسی برق، دانشگاه لرستان، خرم آباد، ایران | ||
| 3دانشیار گروه مهندسی آب، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه،ایران | ||
| چکیده | ||
| تحلیل کمی و کیفی منابع آب امروزه به یکی از موضوعات مهم در تحقیقات منابع آب تبدیل شده است. در این تحقیق از دادهکاوی، تکنیکهای هوش مصنوعی و ریاضی برای شبیهسازی رفتار آب و تخمین تغییرات پارامتریک آن استفاده شده است. نام مدلهای بکار گرفته شده عبارتند از: مدل ماشین یادگیری نیرومند خودتطبیق SAELM، حداقل مربعات ماشین بردار پشتیبان LSSVM، مدل شبکههای عصبی نروفازی ANFIS و مدل آماری رگرسیون خطی چندگانه MLR که برای تخمین پارامترهای هیدروژئولوژیکی استفاده شده است. همچنین برای ارزیابی عملکرد مدلها، در قالب 5 رویکرد دقت مدلها بررسی گردید. نتایج تحقیق نشان داد که براساس نمودارهای شبیهسازی و همبستگی مدل SAELM برترین مدل بود. براساس شاخصهای ارزیابی دقت، مدل SAELM با شاخصهای RMSE و MAPE و R به ترتیب برابر با 1545/0، 0070/0 و 9979/0 دارای بالاترین دقت در تخمین پارامترهای هیدروژئولوژیکی بود. بر اساس تحلیل عدم قطعیت ویلسون (Wilson Score method) عملکرد مدل برتر (SAELM) دست پایین (Underestimated) برآورد گردید. همچنین براساس نمودارهای نسبت اختلاف خطا، دقیقترین نتایج مربوط به مدل SAELM بود. در پایان با استفاده از نمودارهای توزیع خطا کمترین میزان خطا به مدل SAELM اختصاص یافت. | ||
| کلیدواژه‌ها | ||
| ماشین یادگیری نیرومند خودتطبیق؛ ماشین یادگیری حداقل مربعات بردار پشتیبان؛ شبکههای عصبی نروفازی؛ رگرسیون خطی چندگانه؛ تحلیل عدم قطعیت | ||
| عنوان مقاله [English] | ||
| Hydraulic Modeling of the Water Resources using Learning Techniques | ||
| نویسندگان [English] | ||
| Mojtaba Poursaeid1؛ Amirhossain Poursaeid2؛ saeid shabanlou3 | ||
| 1Deputy of Technical and Engineering, Plan and Budget Organization, Khorramabad, Iran | ||
| 2Ph.D student, Department of Electrical Engineering, Faculty of Tecnnical and Engineering, Lorestan University, KHorramabad, Iran | ||
| 3Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran. | ||
| چکیده [English] | ||
| Quantitative and qualitative analysis of water resources has become one of the most widely used topics in water resources research today. In this research, data mining, artificial intelligence, mathematical techniques have been used to simulate water behavior and estimate its parameters changes. The models used to estimate hydrogeological parameters are Self-adaptive Extreme learning machine (SAELM), Least square support vector machine (LSSVM), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple linear regression (MLR) models. Also, to evaluate the performance of these models, the accuracy of the models was assessed in the form of 5 approaches. The results showed that the SAELM model was the best model based on the simulation and correlation diagrams. Based on accuracy evaluation indices, the SAELM model with RMSE, MAPE and, R indices equal to 0.1545, 0.0070, and 0.9979, respectively, had the highest accuracy in hydrogeological parameters prediction. Based on Uncertainty Analysis by the Wilson Score method, the performance of the top model (SAELM) was estimated to be underestimated. Also, based on the error ratio diagrams, the most accurate results were related to the SAELM model. Finally, the SAELM model was assigned the lowest error rate using the error distribution diagrams. | ||
| کلیدواژه‌ها [English] | ||
| Self Adaptive Extreme Learning Machine, Least Square Support Vector Machine, Adaptive Neuro Fuzzy Inference System, Multiple Linear Regression, Uncertainty Analysis | ||
| مراجع | ||
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