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توسعۀ مدل‌های ANN, FIS و ANFIS برای ارزیابی شاخص کفایت در سامانه‌های توزیع آب کشاورزی (مطالعۀ موردی: شبکۀ آبیاری رودشت) | ||
| اکوهیدرولوژی | ||
| مقاله 7، دوره 7، شماره 3، مهر 1399، صفحه 635-646 اصل مقاله (877.44 K) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ije.2020.302414.1327 | ||
| نویسندگان | ||
| حبیبه شریفی1؛ عباس روزبهانی* 2؛ سید مهدی هاشمی شاهدانی2 | ||
| 1دانشجوی کارشناسی ارشد منابع آب، گروه مهندسی آبیاری، پردیس ابوریحان، دانشگاه تهران | ||
| 2دانشیار، گروه مهندسی آبیاری، پردیس ابوریحان، دانشگاه تهران | ||
| چکیده | ||
| بهمنظور مدیریت صحیح آب در بخش کشاورزی، بهبود مدیریت بهرهبرداری سامانه‌های توزیع آب کشاورزی و ارزیابی آنها ضروری است. در تحقیق حاضر برای تحقق هدف یادشده، از مدل‌های سیستم استنتاج فازی (FIS)، شبکۀ عصبی (ANN) و سیستم استنتاج فازی-عصبی تطبیقی (ANFIS)، برای توسعۀ مدل هوشمند ارزیابی کفایت تحویل آب در یک کانال آبیاری، با در نظر گرفتن عدم قطعیتهای موجود در فرایند بهرهبرداری استفاده شد. بهمنظور توسعه و بررسی چگونگی عملکرد مدل‌های توسعه داده‌شده، کانال اصلی شبکۀ آبیاری رودشت در استان اصفهان که با مشکل نوسان‌های شدید جریان ورودی روبهرو است، به عنوان مطالعۀ موردی انتخاب شد. از مدل شبیه‌ساز هیدرودینامیکی HEC-RAS، برای تولید اطلاعات مورد نیاز آموزش و صحتسنجی مدل‌های یادشده استفاده شد. نتایج نشان داد براساس شاخص آماری MAPE، ساختارهای منتخب در دو مدل ANN و ANFIS در تخمین شاخص کفایت تحویل آب کشاورزی نسبت به مدل FIS، به‌ترتیب به مقدار 07/57، 68/56 درصد بهبود یافته است. بررسی نتایج نشان داد مدل‌های توسعه داده‌شدۀ هوشمند نسبت به روشهای مرسوم ارزیابی (مدل‌های هیدرودینامیکی و شاخصهای ارزیابی) نهتنها زمانبر نبوده بلکه با در نظر گرفتن عدم قطعیت، نتایج دقیقی را ارائه میدهند. همچنین، مدل‌های ANN و ANFIS نسبت به FIS عملکرد بهتری داشتند، بنابراین قابلیت استفاده برای سایر سامانه‌های توزیع آب کشاورزی را نیز دارند. | ||
| کلیدواژه‌ها | ||
| توزیع آب کشاورزی؛ کفایت تحویل؛ FIS؛ ANN و ANFIS | ||
| عنوان مقاله [English] | ||
| Development of ANN, FIS and ANFIS Models to Evaluate the Adequacy Index in Agricultural Water Distribution Systems (Case study: Rudasht Irrigation Network) | ||
| نویسندگان [English] | ||
| Habibeh Sharifi1؛ Abbas Roozbahani2؛ Mehdi Hashemy Shahdany2 | ||
| 1MSc Student of Water Resources Engineering, Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran, Tehran, Iran | ||
| 2Associate Professor, Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran, Tehran, Iran | ||
| چکیده [English] | ||
| In order to properly manage water in the agricultural sector, it is necessary to improve the management of agricultural water distribution systems as well as their evaluation. In this research, to achieve this goal, the models of Fuzzy Inference System (FIS), Artificial Neural Network (ANN) and Adaptive Fuzzy Neural Inference System (ANFIS), to develop a smart model for analyzing the adequacy of water delivery in an irrigation canal, given uncertainty. In order to develop and evaluate the performance of the developed models, the main canal of Rudasht Irrigation Network in Isfahan province, which is facing the problem of severe fluctuations in the inflow, was selected as the case study. The HEC-RAS hydrodynamic simulator model was used to generate the information needed to train and validate these models. The results showed that according to the MAPE index, the selected structures in ANN and ANFIS models in estimating the adequacy index of agricultural water delivery compared to FIS model have improved by 57.07% and 56.68%, respectively. Evaluation of the results showed that the developed models compared to conventional evaluation methods (hydrodynamic models and evaluation indicators) not only did not take time but also provided more accurate results, considering the uncertainty and also, ANN and ANFIS models performed better than FIS, so they can be used for other agricultural water distribution systems. | ||
| کلیدواژه‌ها [English] | ||
| Agricultural Water Distribution, Delivery Adequacy, FIS, ANN, ANFIS | ||
| مراجع | ||
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[1]. Shahdany SMH, Sadeghi S, Majd EA. Application of Automatic Regulating Structures in Order to Improving Main Irrigation Canal Ooerational Performance Suffering From Severe Inflow Flucations, Case Study of Roodasht Main Irrigation Canal. 2017; 14-27. [Persian] [2]. Hosseini SM, Mosaedi A, Golkarian A, Nasseri K. Modeling Some Factros of Affecting Rill Erosions using Fuzzy Inference System. Water and Soil Conservation. 2015; 22(4): 103-120. [Persian] [3]. Poustizadeh N, Najafi N. Discharge Prediction by Comparing Artificial Neural Network With Fuzzy Inference Sysytem(Case study: Zayandeh rud River). Iran Water Resources Research. 2011; 7(2): 92-97. [Persian] [4]. Koorehpazan Dezfuli, A. Fuzzy Set Theory and its Applications in the Modeling of Water Engineering Problems. Amirkabir Jahad Daneshgahi Press, Tehran. 2005; 261p. [Persian] [5]. Poustizadeh, N. River Flow Forecasting Using Fuzzy Inference System, M.Sc. Thesis, Tarbiat Modares University. 2006; 153p. [Persian] [6]. Ashrafi KH, Hoshiaripoor GA, Najararabi B, Keshavarzi Shirazi H. Prediction of Daily Carbon monoxide Concentration Using Hybrid FS-ANFIS Model Based on Atmospheric Stability Analysis; Case Study: city of Tehran. Journal of the Earth and Space Physics. 2012; 38(2): 183-201. [7]. Milan, SG, Roozbahani A, Banihabib ME. Fuzzy optimization model and fuzzy inference system for conjunctive use of surface and groundwater resources. Journal of Hydrology. 2018; 566: 421-434 [8]. Tiri A, Belkhiri L, Mouni L. Evaluation of surface water quality for drinking purposes using fuzzy inference system. Groundwater for Sustainable Development. 2018; 6: 235-244. [9]. Ahmed AAM, Shah SM A. Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River. Journal of King Saud University-Engineering Sciences. 2017; 29(3): 237-243 [10]. Yaghoubi B, Izadbakhsh, MA, Hayati F. Predicting discharge coefficient of Triangular Plan Form Weirs using Hybrid Model based on Fuzzy Systems and Differential Evolution Algorithm. Journal of Dam and Hydroelectric Powerplant. 2019; 6(22): 1-12. [Persian]
[11]. Esmaeili Gisavandani H, Akhond Ali AM, Zarei H, Taghian M. Evaluation of the Ability of Adaptive Neuro Fuzzy System Artificial Neural Network and Regression to Regional Flood Analysis.Water and Soil Conservation. 2017; 24(3): 149-166. [Persian]
[12]. Yaseen ZM, Ebtehaj I, Bonakdari H, Deo RC, Mehr AD, Mohtar WHMW, et al. Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model. Journal of Hydrology. 2017; 554: 263-276.
[13]. Tao H, Diop L, Bodian A, Djaman K, Ndiaye PM, Yaseen ZM. Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: Regional case study in Burkina Faso. Agricultural water management. 2018; 208: 140-151.
[14]. Nguyen V, Li Q, Nguyen L.Drought forecasting using ANFIS-a case study in drought prone area of Vietnam. Paddy and water environment. 2017; 15(3): 605-616
[15]. Yaseen ZM, Ghareb MI, Ebtehaj I, Bonakdari H, Siddique R, Heddam S,et al. Rainfall pattern forecasting using novel hybrid intelligent model based ANFIS-FFA. Water resources management. 2018; 32(1): 105-122.
[16]. Wen X, Fang J, Diao M, Zhang C. Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China. Environmental monitoring and assessment. 2013; 185(5): 4361-4371.
[17]. Shadmani M, Marofi S, Mohammadi K, Sabziparvar AA. Regional flood discharge modeling in Hamedan province using Artificial Neural Network. Journal of Water and Soil Conservation. 2011; 18(4): 21-42. [Persian]
[18]. MalekZadeh M, Fereydooni M. Evaluation of MLP Neural Network in Flow Discharge Prediction in Tangabad Dam Basin FiroozAbad River. Indian Journal of Fundamental and Applied Life Sciences. 2015; 5(4): 322-329.
[19]. Cheng Ch, Niu W, Feng Zh, Shen j, Chau K. Daily Reservoir Runoff Forecasting Method Using Artificial Neural Network Based on Quantum-behaved Particle Swarm Optimization. Water. 2015; 7: 4233-4246.
[20]. Seo Y, Kim S, Kisi O, Singh VP. Daily water level forecasting using wavelet decomposition and artificial intelligence techniques. Journal of Hydrology. 2015; 520: 224-243.
[21]. Kaghazchi A. Development of Bayesian Network Model for Simulation and Assessment of Water Distribution and Delivery within Main Irrigation Canals. Master Thesis, Irrigation and Drainage Department, Aburaihan Campus, University of Tehran. 2019. [Persian]
[22]. Molden DJ, Gates, T. K. Performance measures for evaluation of irrigation-water-delivery systems. Journal of irrigation and drainage engineering. 1990; 116(6): 804-823.
[23]. Ghasemnezhad Moghadam N, Baghaienia F, Bafandeh Zendeh A. Fuzzy logic in simple language. Iranian Magazine of Quality Control. 1999; 24: 43-51.
[24]. McCulloch WS, Pitts W. A logic calculus of the ideas imminent in nervous activity. Bull Math Biophys. 1943; 5: 115-33
[25]. Rosenblatt F. Priciples of Neurodynamics: Perceptrons and the Theory of Brain Mechanics. Spartan. 1962.
[26]. Noori N, Kalin L. Coupling SWAT and ANN models for enhanced daily streamflow prediction. Journal of Hydrology. 2016; 533: 141-151.
[27]. Kasiviswanathan KS, Sudheer KP. Comparison of methods used for quantifying prediction interval in artificial neural network hydrologic models. Modeling Earth Systems and Environment. 2016; 2(1): 22
[28]. Radmanesh F, Pourhaghi A, Solgi A. Improving the Performance of ANN Model, Using Wavelet Transform and PCA Method for Modeling and Predict Biochemical Oxygen Demand (BOD). Journal of the Ecohydrology. 2017; 3(4): 569-585. [Persian]
[29].Alborzi M. Introduction to Artificial Neural Networks. Amirkabir University of Technology Press. 2002; 137p. [Persian]
[30]. Asghari J, Rostami R. Monthly Discharge Prediction of Seminehrood Using Support Vector Machin and Intergrated Fuzzy Neural Inference System. Icohacc. 2017; 1-9. [Persian]
[31]. Rezaei Navaei S, Koosha H. Applying Data Mining Techniques for Customer Churn Prediction in Insurance Industry.International Journal of Industrial Engineering & Production Management. 2016; 635-653. [Persian] | ||
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