پيش بيني هندسه هيدروليكي پايين دست كانال هاي آبرفتي با استفاده از الگوريتم هوشمند مختلط (GMDH-HS)

Prediction of downstream hydraulic geometry in alluvial channels using the hybrid intelligent algorithm (GMDH-HS)

هندسه هيدروليكي پايين دست كانال هاي آبرفتي در شرايط دبي لبريز توسط پارامترهاي عرض كانال، عمق متوسط جريان، شيب كانال و سرعت متوسط جريان تعريف مي‌شود. اين متغيرها توسط پارامترهاي مستقل دبي جريان، قطر متوسط ذرات بستر و پارامتر شيلدز قابل تعريف هستند. در اين تحقيق عملكرد مدل هوشمند داده محور GMDH و مدل هوشمند مختلط GMDH-HS براي بيان بهترين رابطه بين متغيرهاي هندسه هيدروليكي پايين دست كانال‌هاي آبرفتي بر حسب پارامترهاي مستقل مورد بررسي قرارگرفته است. 880 سري داده اندازه گيري شده از رودخانه ها و كانال هاي آزمايشگاهي تحت شرايط جريان و بسترهاي متفاوت جمع آوري شد، كه 498 سري داده براي آموزش و مابقي براي ارزيابي مدل ها استفاده گرديد. صحت سنجي مدل هاي توسعه داده شده با استفاده از سري زماني مكي-گلاس انجام گرفت. ارزيابي عملكرد مدل هاي توسعه داده شده با استفاده از شاخص هاي آماري CE، MSRE، MAPE، RMSE، RB و R2 حاكي از عملكرد رضايت بخش هر دو مدل در پيش بيني هندسه هيدروليكي پايين دست كانال هاي آبرفتي است. بررسي دقيق تر و مقايسه نتايج دو مدل براي هر چهار متغير نشان داد كه مدل GMDH-HS در پيش بيني هندسه هيدروليكي پايين دست كانال هاي آبرفتي عملكرد بسيار بالاتري داشته است.

Introduction Alluvial rivers always change their hydraulic geometry to achieve a balance between water discharge, input and output sediment. Hydraulic geometry focuses specifically on the evolution of the river form and how the bed and channel influence this evolution. The morphology of alluvial rivers has led to the creation of two concepts: (1) at-a-station hydraulic geometry and (2) downstream hydraulic geometry (Julien, 2015). Downstream hydraulic geometry is defined by the top channel width (W), average flow depth (h), mean flow velocity (V), and slope of the flow energy (S) under bankfull conditions. Downstream hydraulic geometry as a function of hydraulic parameters and bed conditions, including flow rate, median size of bed particles and the Shields parameter is paramount to determine the state of a river. Therefore, various relationships have been derived based on various methods to estimate the channel hydraulic geometry, include: empirical relationships based on collected fields observation and theoretical relationships based on governing equations such as flow rate, resistance to flow, secondary flow and sediment transport in alluvial river. Result of theoretical derivations indicated reasonable agreement with field observations and regime equations. In recent years, intelligent data driven methods is used as new methods for predicting and estimating the parameters of complex hydraulic models. One of the common methods is the Group Method of Data Handling (GMDH) with self-organization approach, which has the ability to solve complex non-linear problems with higher accuracy and simpler structure. In GMDH method the coefficients of the polynomial are found by a Least Square Estimation (LSE) method. It is possible that combined the GMDH methods and optimization algorithms. By doing this, a hybrid method will be created. In this method an optimization algorithm used to calibrate the weights of each neuron in GMDH rather than LSE method and so the hybrid methods may have better performance. The GMDH method is combined with artificial intelligence and optimization techniques such as harmony search (HS) optimization method. Harmony search algorithm (HS) is one of the optimization methods that used to solve nonlinear problems, which was introduced in 2001 by Geem et al. based on a metaheuristic technique. The advantages of this algorithm are less computations to find a solution, fast convergence and significant ability to achieve the optimal solution due to the appropriate structure. HS algorithm has become one of the most used optimization algorithms because can be used for both continuous and discrete problems. Since the GMDH algorithm has a self-organizing approach and its structure is initially unclear, the Harmony search algorithm is used to train and optimize the weights in the structure of each neuron in the GMDH network. In fact, the objective of HS sub model is to determine the optimal weights in short time to achieve the optimal GMDH structure and minimize the cumulative error between the measured and computed data sets.