فهرست مطالب

دانش آب و هیدرولیک - سال سی و چهارم شماره 4 (زمستان 1403)

نشریه دانش آب و هیدرولیک
سال سی و چهارم شماره 4 (زمستان 1403)

  • تاریخ انتشار: 1403/10/01
  • تعداد عناوین: 6
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  • علی اکبر کرموند* صفحات 1-19

    شبیه سازی جریان رودخانه به با دقت بالا لازمه علم مدیریت رودخانه می باشد. در مواجه با چالش قدیمی مدلسازی روزانه جریان رودخانه، آموزش عمیق به عنوان ابزاری نوین مطرح شده است. در مطالعه حاضر، با تمرکز بر انتخاب سناریوی مناسب از ورودی های مدل آموزش عمیق، شبیه سازی جریان روزانه رودخانه کشکان در چندین نوبت به روش آموزش عمیق LSTM و  GRUانجام شده است. پیش از این، مدلسازی آموزش عمیق به روش GRU و با استفاده از داده های بومی اندازه گیری جریان رودخانه انجام نشده است. منطقه، مستعد سیل و کوهستانی بوده و ایستگاه هیدرومتری با سابقه وقوع سیل، واقع بر روی رودخانه کشکان انتخاب شده است. با استفاده از 4 رویکرد از روش های حذف داده های پرت، ورودی به دو مدل LSTM وGRU انتخاب شده و هشت مدل تولید شده است. ورودی های ممکنه، عبارت بوده است از میانگین بارش منطقه، شاخص پوشش گیاهی نرمال شده، رطوبت خاک سطحی، جریانات آب زیرزمینی و همچنین خود جریان رودخانه کشکان در ایستگاه هیدرومتری. نتایج نشان داد بهترین عملکرد را به ترتیب، مدل GRU با ورودی های اصلاح شده به روش حذف Z-Score، ماهالانوبیس با مقادیر RMSE میانگین و KGE و 41/5 و 99/0 و 23/6 و 7/0 در آموزش و 17/8 و79/0و 21/4 و 81/0در اعتبارسنجی و 01/5 و 68/0 و21/7 و 52/0و در مرحله تست می باشند. نتایج، روش LSTM را در شبیه سازی جریان رد نمی کند، اما سناریوهای برشمرده شده در روش GRU قدرت بالاتری در تشخیص الگوی پیچیده جریان روزانه رودخانه نشان دادند.

    کلیدواژگان: آموزش عمیق، پیش بینی، جریان رودخانه، حذف داده های پرت، علم داده
  • رعنا صدیق پور، حسین رضایی*، حسن گل محمدی صفحات 21-38

    ناهماهنگی بین وقوع زمان بارندگی حدی و جاری شدن سیلاب، از مظاهر شایان توجه تغییرات در کاربری اراضی می باشد. در تحقیق حاضر، از 21 ایستگاه باران سنجی و 17  ایستگاه هیدرومتری در حوضه تالاب گاوخونی از داده های حدی استفاده شده تا علل خشک شدگی تالاب مورد ارزیابی قرار گیرد. متوسط وقوع زمانی هر پدیده حدی، باند اطمینان، واریانس، انحراف معیار و انحراف معیاراستاندارد برای ایستگاه ها از آمار جهت دار و آزمون ریلیه برای تفسیر نتایج استفاده شده است. از آزمون ریلیه برای بررسی اینکه آیا جمعیت به طور یکنواخت در اطراف دایره توزیع شده است یا خیر استفاده شد. متوسط شاخص فصلی بودن (R، پراکندگی) وقوع زمان بارش در کل ایستگاه های باران سنجی بیشتر از 6/0 (60 درصد) محاسبه شد. محدوده ی بارش های حدی در تمام ایستگا ها در بازه 83 روز (800/81-) تا 20 روز (650/19) قبل از سال جدید شمسی اتفاق می افتد. متوسط شاخص فصلی بودن برای ایستگاه های هیدرومتری وقوع زمان سیلاب حدی از 27/0 تا 87/0 متغیر می باشد. از میان 17 ایستگاه هیدرومتری، خروجی 9 ایستگاه تحت تاثیر سد مخزنی یا انحرافی موجود در بالادست نبوده که ضریب شاخص فصلی بودن آن ها بیشتر از 6/0 محاسبه شده است. نتایج آزمون ریلیه نشان داد که توزیع بارش و رواناب این حوضه یکنواخت نیست و دارای خاصیت فصلی است.

    کلیدواژگان: آمار جهت دار، آمار دایره ای، تالاب گاوخونی، شاخص فصلی بودن، کاربری اراضی
  • سپیده نظری، محمد بی جن خان*، هادی رمضانی اعتدالی، علی مهدوی صفحات 39-51

    در این پژوهش به بررسی عملکرد شیرهای خودکار کنترل دبی برای تنظیم آب تحویلی در سطح مزرعه پرداخته شد. در این راستا، شیرهای خودکار با ظرفیت دبی عبوری 5 و 10 لیتر بر ثانیه در یک مزرعه مجهز به سامانه آبیاری نواری نصب و اثر استفاده از این شیرها به صورت میدانی بررسی شد. همچنین، هیدرولیک سامانه آبیاری قطره ای نواری با استفاده از داده های بدست آمده در حالت قبل و بعد از نصب شیرهای کنترل دبی، با نرم فزار EPANET مدل سازی شدند. مدل ریاضی با استفاده از داده های میدانی واسنجی شد. سپس، دو سناریوی اثر تغییرات رقوم سطح آب در استخر و پارگی نوارهای آبیاری مورد بررسی قرار گرفت. در بررسی تاثیر عملکرد تغییر ارتفاع آب استخر، بدون استفاده از شیرهای خودکار، با افزایش رقوم آب در استخر، مقدار آب ورودی به سامانه آبیاری افزایش می یابد. این در حالی است که با استفاده از شیر خودکار کنترل دبی، با تغییر ارتفاع آب در استخر، مقدار آب ورودی به سامانه آبیاری تغییر نکرده و ثابت باقی می ماند. با بررسی عملکرد شیر در هنگام پارگی و یا در رفتگی نوارهای آبیاری در شرایط بدون استفاده از شیرهای خودکار با ایجاد پارگی در نوارهای آبیاری، مقدار دبی کل سامانه آبیاری افزایش می یابد. این در حالی است که با استفاده از شیرهای کنترل، مقدار دبی در سامانه تقریبا ثابت می ماند. بنابراین شیرهای کنترل دبی در برابر نوسانات فشار ناشی از تغییر ارتفاع آب در استخر و یا پارگی نوارهای آبیاری، مقدار دبی ثابتی عبور داده و سبب افزایش توزیع یکنواختی زیر واحدهای آبیاری می شوند.

    کلیدواژگان: آبیاری قطره ای نواری، سامانه های آبیاری تحت فشار، شیرهای کنترل دبی، صرفه جویی، نرم افزار EPANET
  • علی حمیدزاده، سعید صمدیان فرد* صفحات 53-74

    دمای نقطه شبنم یکی از کاربردی ترین پارامترهای هواشناسی است که در علم مکانیک (بخش تهویه و مطبوع) و کشاورزی بسیار مورد استفاده قرار می گیرد. در این پژوهش، توانمندی مدل های رگرسیون بردار پشتیبان، مدل درختی، مدل تولید قوانین از مدل درختی، مدل فرآیند گاوسی، مدل رگرسیون خطی، مدل جنگل تصادفی و مدل درخت تصادفی در تخمین دمای نقطه شبنم مورد ارزیابی قرار گرفته است. داده های هواشناسی روزانه دو ایستگاه گرگان و شهرکرد، برای بازه زمانی سال های 1990 تا 2021 استفاده شده است. همچنین، پارامترهای هواشناسی دمای میانگین، دمای حداکثر، دمای حداقل، میانگین سرعت باد، ساعات آفتابی، میانگین رطوبت نسبی، حداکثر رطوبت نسبی، حداقل رطوبت نسبی نسبت به هم ارزیابی و 8 سناریوی مختلف به عنوان پارامتر ورودی برای هر مدل در نظر گرفته شد. نتایج نشان داد که مدل رگرسیون بردار پشتیبان، برای سناریوی 8، با جذر میانگین مربعات خطا 222/0، معیار انحراف خطا 092/0، میانگین خطای مطلق 147/0، شاخص توافق ویلموت 1 در ایستگاه گرگان و مدل رگرسیون بردار پشتیبان برای سناریوی 7، با جذر میانگین مربعات خطا 55/0، معیار انحراف خطا 285/0، میانگین خطای مطلق 346/0، شاخص توافق ویلموت 997/0 در ایستگاه شهرکرد، به عنوان مدل های برتر برای تخمین دمای نقطه شبنم روزانه هستند. در نهایت، روش رگرسیون بردار پشتیبان، به عنوان روشی توانمند در پیش بینی دمای نقطه شبنم برای استفاده در مقاصد کشاورزی معرفی گردید.

    کلیدواژگان: دمای نقطه شبنم، شهرکرد، مدل های درختی، گرگان، مدل های رگرسیونی
  • سارا نامدار، اکرم عباسپور*، علی حسین زاده، فرزین سلماسی صفحات 75-88

    سرریزها از مهم ترین سازه های اندازه گیری و تنظیم دبی جریان هستند. از سرریزها، برای کنترل سطح آب و اندازه گیری دبی جریان در کانال ها استفاده می شود. سرریزهای جانبی گاه باعنوان سرریز کناری هم شناخته می شوند. سرریزهای جانبی معمولا به شکل های هندسی مختلف از جمله مستطیلی، قوسی، ذوزنقه ای و مثلثی ساخته می شوند. در سرریزهای جانبی ذوزنقه ای شکل، ارتفاع سرریز، بار آبی و زاویه دیواره سرریز بر ضریب دبی جریان تاثیر دارد. در این تحقیق، به صورت عددی با استفاده از نرم افزار فلوئنت، تاثیر پارامترهای زاویه راس دیواره ی سرریز، ارتفاع سرریز و بار آبی بر روی ضریب دبی سرریز جانبی ذوزنقه ای مورد بررسی قرار گرفت و نتایج به دست آمده با نتایج حاصل از داده های آزمایشگاهی مقایسه گردید. نتایج حاصل از شبیه سازی، بسیار نزدیک به نتایج آزمایشگاهی به دست آمد. مقادیر خطای نسبی در بازه 5 تا 7 درصد به دست امد.  با توجه به این نتایج، مقادیر ضریب دبی آزمایشگاهی با مقادیر شبیه سازی شده تطابق خوبی دارد. ضریب دبی (Cm) وابسته به یک پارامتر هیدرولیکی و یا هندسی نبوده بلکه پارامترهای ارتفاع نسبی سرریز، بار آبی، زاویه راس دیواره سرریز بر آن تاثیر گذار هستند. نتایج به دست آمده نشان داد که، سرریز جانبی با شیب دیواره 45 درجه (z=1) نتایج بهتری در مقایسه با دو زاویه مورد بررسی دیگر دارد، چون دارای بیشترین مقدار ضریب دبی در بین سرریزهای مختلف است. همچنین می توان نتیجه گرفت مقادیر سرعت در نزدیک سرریز جانبی با شیب بیش تری  کاهش می یابد و به طرف پایین دست در کانال اصلی،  تغییرات سرعت کاهش می یابد.

    کلیدواژگان: سرریز جانبی ذوزنقه ای، ضریب دبی، مدل های تلاطم، نرم افزار فلوئنت
  • باقر نیکوفر*، وحید نورانی صفحات 89-110

    یکی از مهم ترین فاکتورهای مدیریتی در دوران بهره برداری مخازن سدها، تعیین پارامترهای بهینه بهره برداری می باشد. با توجه به اینکه حجم رهاسازی در ارتباط با حجم ذخیره مخازن سدها بوده و بایستی تواما و باهم بهینه سازی گردند، لذا در این تحقیق تلاش می گردد، پس از معرفی الگوریتم ژنتیک و الگوریتم ازدحام ذرات، عملکرد این الگوریتم ها به تنهایی و در حالت ترکیب با هم، در بهره برداری بهینه از مخزن سد علویان با نتایج مدل سازی برنامه ریزی غیرخطی مقایسه و منحنی های فرمان بهره برداری ترسیم گردند. برای ارزیابی عملکرد الگوریتم های مورد بررسی در بهره برداری بهینه از مخزن، از شاخص های عملکرد مخزن استفاده شده است. جواب بهینه مدل های الگوریتم ژنتیک و الگوریتم ازدحام ذرات به ترتیب با 08/1 و 87/0 و الگوریتم ترکیب آن ها با مقدار 62/0 و روش برنامه ریزی غیرخطی جواب بهینه محلی 91/0 می باشند. با توجه به شاخص های عملکرد مخزن، الگوریتم ترکیبی توانسته است 85 درصد از نیاز آبی کشاورزی پایاب سد علویان را تامین کند. جواب های بهینه نشان دادند که مدل الگوریتم ترکیبی در مورد سیاست بهره برداری از مخزن، نتیجه مطلوب تری داشته و نتایج حاکی از عملکرد بالای الگوریتم ترکیبی در مقایسه با دیگر روش های مورد بررسی در بهره برداری بهینه از سیستم تک مخزنه سد علویان بود. بر این اساس، پارامترهای بهینه بهره برداری از مخزن سد علویان با استفاده از الگوریتم ترکیبی به دست آمد.

    کلیدواژگان: بهره برداری بهینه، الگوریتم های ازدحام ذرات و ژنتیک، الگوریتم ترکیبی، هیدروانفورماتیک، منحنی فرمان، مخزن سد علویان
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  • Aliakbar Karamvand * Pages 1-19
    Background and Objectives

    In recent years, the use of artificial intelligence methods, such as artificial neural network models, have become increasingly prevalent in simulating complex natural phenomena, including daily streamflow. The streamflow directly correlates with flood occurrences, and mitigating financial and human losses due to floods is crucial. Accurate streamflow simulation is essential for water resource management and river management. Consequently, in hydrology, deep learning methods have emerged as novel tools to address the longstanding challenge of daily streamflow modeling and are widely used in simulations.Advancements in streamflow modeling with Artificial Intelligence (AI): In recent years, the field of hydrology has witnessed a significant shift toward leveraging AI techniques for streamflow modeling. Among these methods, artificial neural network (ANN) models have gained prominence due to their ability to capture complex relationships within hydrological systems. Streamflow, which represents the flow of water in rivers and streams, is a critical variable for understanding water availability, flood risk, and ecosystem health. By accurately simulating streamflow, researchers and water resource managers can make informed decisions regarding water allocation, flood preparedness, and environmental conservation. Hydrological processes are inherently nonlinear and influenced by various factors such as precipitation, temperature, land cover, and soil properties. Traditional hydrological models often struggle to capture these complexities. However, deep learning methods, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), offer promising solutions. These models can learn intricate patterns from historical streamflow data, adapt to changing conditions, and provide accurate predictions. As a result, they have become indispensable tools for addressing the longstanding challenge of daily streamflow modeling. Researchers continue to explore novel architectures, data augmentation techniques, and hybrid approaches to enhance the performance and robustness of AI-based streamflow simulations. In summary, the integration of deep learning methods into hydrological research has revolutionized streamflow modeling, enabling more accurate predictions and informed decision-making in water management and flood risk assessment.

    Methodology

    In this study, we focused on selecting an appropriate input scenario for deep learning models and simulate daily streamflow on the Kashkan River using LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) deep learning methods. Prior to this, deep learning modeling with the GRU approach using native streamflow measurements had not been performed for Kashkan river. The study area is a flood-prone and mountainous region, specifically the western part of Iran, where a hydrological station with a history of flood events is situated on the Kashkan River. We employ four approaches for handling outliers (Mahalanobis, critical interval removal, Z-Score, and no removal) and four different preprocessing techniques for input data to train two models: LSTM and GRU. Ultimately, eight distinct models are generated and validated against historical data. The input features include regional average precipitation, normalized vegetation cover index, surface soil moisture, groundwater flow, and the Kashkan River’s own flow at the hydrological station, with the best features selected using statistical correlation control.

    Findings

    The results demonstrate that among the deep learning models generated with a 10-day time step, the model with the least error and consistent low error retention in error metrics is observed. Furthermore, the best performance is achieved using different approaches, in the following order: the GRU model with Z-Score-corrected inputs, followed by the Mahalanobis removal approach with average RMSE (Root Mean Square Error) and KGE (Kling-Gupta Efficiency) values of 5.41 and 0.99, respectively, and the critical interval removal approach with RMSE of 6.23 and KGE of 0.7.The results showed that among the deep learning models produced with a time step of 10 days in the model, the lowest amount of error and the persistence of low error can be seen in the error statistics, and among the different approaches used, the best performance is the GRU model with input modified by Z-Score elimination of outlier method, Mahalanobis elimination method with average RMSE and KGE values of 5.41, 0.99, 6.23, and 0.7 in the training phase and 8.17, 0.79, 4.21, and 0.81 in the validation phase and 5.01, 0.68, and 7.21 and 0.52 are in the testing phase. The obtained results do not reject the LSTM method in simulating the river flow, but state that the listed scenarios, especially in the GRU method, have a higher power in dealing with the data and recognizing the complex pattern of daily river flow, taking into account the limitation in use They have seven years of regular daily data, and future research will show how the behavior of GRU and LSTM models will differ if data with higher convergence is used.

    Conclusion

    GRU in future studies can make difference by enhanced flood forecasting accuracy, efficient computation and real-time applications, integration with lag time preprocessing, adaptability to changing climate and urbanization. Future studies will be on data driven method in flood prone areas. There remains ample room for future research and innovation. Here are some directions for further exploration: hydrological data fusion, spatially explicit models, uncertainty quantification, climate change resilience.

    Keywords: Deep Learning, Forecasting, Hydrology, RNN, Streamflow
  • Rana Sedighpour, Hossein Rezaei *, Hassan Golmohammadi Pages 21-38
    Background and Objectives

    Regarding water supply, water resource management is very important. Rainfall and floods are among the climate variables that play an important role in water management and agriculture. The inconsistency between the occurrence of the maximum rainfall and the flooding is one of the noteworthy manifestations of changes in land use. Knowing the reasons for the inconsistency between the time interval of rains and extreme floods can potentially be used to advance agricultural programs, water resources management, flood prevention, groundwater feeding, natural resources management, land use, industry and national economy should be important. Any extreme rainfall causes a flood at any subsequent time. In the case of annual analysis of the occurrence of extreme rainfall and floods, valuable information can be obtained from the state of land use or water resources.

    Methodology

    Today, various statistical methods along with efficient software are used to check rainfall and flood data. In the meantime, we can mention the method of directional statistics in MATLAB environment with coding, which has attracted the attention of many experts and researchers. In fact, circular statistics is a branch of statistics that is dedicated to the development of statistics and supports special data such as directional data. Therefore, for the statistical analysis of extreme data or any data that has a time frequency, the use of directional statistics, which is also called circular statistics, is applicable. In this study, Circular statistics have been used to investigate the range of time of occurrence and distribution of threshold data regarding rainfall and runoff from 21 rain gauge stations and 17 hydrometric stations in the Gavkhoni wetland.

    Findings

    The average index of seasonality (dispersion) of time occurrence in most stations is above 0.6 or (60%) and their variance is less than 0.4. Due to the fact that the rainfall in the region is recorded in the stations, the occurrence values of the maximum rainfall during different years have been almost without manipulation or any human factors (influenced by human factors). Therefore, the time of occurrence of maximum rainfall was mostly in the area of 0 to -π/2, and the highest value of was from -81.80⁰ to -19.65⁰ for Lange and Isfahan stations, respectively. The average seasonality index (dispersion) of time occurrence was calculated from 0.27 at Diziche station to 0.87 at Ghale Shahrokh station. The input in some hydrometric stations has undergone changes in different years, or it may have been blocked or deviated before entering some stations. So the time of maximum runoff in those stations will not coincide with the time of rainfall. Therefore, the occurrence time of extreme floods is mostly scattered, and the highest value of is calculated from 161.25⁰ to -43.63⁰ for Diziche and Heydari stations, respectively. In Gavkhoni wetland basin, 9 out of 17 stations are not affected by the dam and 8 other stations are affected by the dam (hydrometric stations immediately after the reservoir or diversion dam).The Rayleigh test rejects the null hypothesis (non-uniformity of flood occurrence in the perimeter of the circle) in all the stations, except for Diziche station. For this reason, it is not possible to calculate the upper and lower confidence band at Diziche station.

    Conclusion

    The last hydrometric station for draining the runoff to the Gavkhoni lagoon is the Varzane station, which has an average seasonality index and the time of the maximum flood output on the 2nd day of the new year. In general, it is affected by the changes of land use in the catchment area. In almost three seasons, spring, autumn and winter, it has output and runoff extreme data, so it shows that the water resources stored upstream of Varzaneh station are consumed and the remaining sewage is discharged from the station with a very low flow rat and it is transferred to Gavakhuni wetland. Therefore, the reason for the dryness of the wetland is being deprived of the natural source of Zayandeh River and it can be concluded that with directional statistics and histogram curves of rainfall and runoff extreme data, land use changes can be detected compared to rainfall and runoff and from the average seasonality index. And another use of indicates that the water intake valves for agricultural and industrial uses should be closed so that the runoff is transferred to the wetland.

    Keywords: Circular Statistics, Directional Statistics, Gavkhoni Wetland, Land-Use, Seasonality Index
  • Sepideh Nazari, Mohammad Bijenkhan *, Hadi Ramezani Etedali, A Mahdavi Pages 39-51
    Background and Objectives

    Water shortage is the main challenge for agriculture in arid and semi-arid regions. Therefore, improving water productivity using different methods such as implementing pressurized irrigation systems is necessary. In these systems the both efficiency and distribution coefficients are vital. The purpose of this study is to investigate the performance of flow control valves in regulating the volume of water delivered in the field and saving water consumption. A flow control valve is recommanded to deliver an almost constant flow for different pressure ranges.

    Methodology

    In this study, the performance of automatic flow control valves to adjust the water delivered in the field was investigated. For this purpose, two valves with flow rates of 5 and 10 Ls-1 were installed on a farm equipped with a drip irrigation system to evaluate the effect of the valves in a field condition. The farm is located in Mahdiabad of Takestan. The area of this field is about 12 ha. Also, EPANET software is used to model the irrigation system in different scenarios. This software can simulate the behavior of water flows in pressurized networks. To model the irrigation system, the specifications of the reservoir, water transmission lines, manifold pipes, and laterals including the length, diameter of the pipes, and elevation, were applied as the input characteristics of the program. Numerical models need to be calibrated to check the correspondence between the measured and simulated parameters. To compare the values measured and simulated by the EPANET model, statistical indices of root mean square error (RMSE), mean absolute error (MBE), and error percentage (NRMSE) were used.

    Findings

    Firstly, the current condition of the farm was evaluated. The results showed the water discharges were 6.4 and 12.35 L s-1 for plots B and F on-farm, respectively. These discharges were 28% and 25.3% higher than the designed discharges for parts B and F, respectively. As a result, water consumption was increased and its surplus was wasted as deep percolation or surface runoff. Then the flow control valves were installed in the suitable places. After installing the flow control valves, the water delivery condition was re-evaluated. Discharges of B and F plots were 4.86 and 9.96 L s-1, respectively. EPANET software was applied to simulate the flow in the irrigation system. The results showed that the flow control valves could be used successfully to deliver an accurate volume of water to the plots and they could compensate the effect of changes in pool water height or ruptures of irrigation tapes. By investigating the effect of changing the height of the pool water, it was found that increasing the height of the water in the pool would result in increasing the irrigation system discharge. However, by installing the flow control valve, water delivery remained almost constant by changing the water height in the pool. Additionally, by examining the performance of the valve during rupture or dislocation of the irrigation tapes, the flow through the tested irrigation fields increased. However, with the use of flow control valves, due to the structural mechanism of them, the flow rate did not exceed from the designed values. Also, the results showed that a dislocation or leakage in the irrigation tapes reduced the pressure and increased the amount of flow consumed by the irrigation system. However, the numerical modeling results showed that with the installation of the automatic control valve, the flow rate of the irrigation tapes remained in a suitable range.

    Conclusion                                                              

    Flow control valves regulate discharge irrespective of the pressure fluctuations. Experiments were performed to identify the ability of a discharge control valve to improve water distribution uniformity in an actual field condition. Field measurements revealed the successful application of the control valves on a farm scale. Also, numerical results indicated that the flow control valves could be used effectively to increase the flow uniformity inthe cases of the water height fluctuations in the reservoir pools or  ruptures of the irrigation tapes. In general, it was found that the flow control valve was a good choice to increase the water efficiency and uniformity in the field conditions. Numerical simulations could determine the suitable locations of the valves and their discharge characteristics.

    Keywords: Drip Irrigation, EPANET Software, Flow Control Valves, Pressure Irrigation Systems, Saving
  • Ali Hamidzadeh, Saeid Samadian Fard * Pages 53-74

    Iran’s agricultural sector faces unique obstacles due to the diversity of its climatic conditions, underscoring the crucial importance of safeguarding crops against the impacts of climate change. One effective strategy to mitigate damage to the agricultural industry is the ability to accurately predict dew point temperature. The dew point refers to the temperature at which water vapor in the air condenses into liquid water or dew, given a constant air pressure. Notably, an excessively high dew point can adversely affect the performance of air conditioning systems and reduce the efficiency of coolant-based ventilation mechanisms. The formation of dew in ecosystems is influenced by a triad of key factors: radiative exchange between the Earth’s surface and the atmosphere, turbulent heat transfer, and vapor pressure. While the dew point is typically measured using a moisture meter, there also exist empirical equations that relate air temperature and humidity. However, reliable dew point forecasting often requires common meteorological parameters, such as relative humidity and precipitation, which are not consistently measured at many weather stations or may be subject to significant error. As such, regression-based estimation methods are frequently employed. Recognizing the importance of data-driven approaches in dew point estimation, this study explores the use of several predictive models, including support vector regression (SVR), the M5P tree model, the M5Rules rule-generation algorithm, Gaussian process regression (GPR), linear regression (LR), random forest (RF), and random tree (RT) models. These models were applied to data collected from two stations in Gorgan and Shahrekord, Iran, to estimate dew point temperature.

    Methodology

    The Gorgan basin is geographically situated between 25°54’ east longitude and 36°50’ north latitude, while the Shahrekord basin is located between 50°49’ east longitude and 32°20’ north latitude. Topographically, Shahrekord lies in the eastern segment of the Zagros Mountain range, along the Zagros fault margin. The input parameters for this study were obtained from the Iran Meteorological Organization, covering the period from 1990 to 2021. These parameters include daily maximum temperature (Tmax), daily minimum temperature (Tmin), daily average temperature (Tm), sunshine hours (sshn), average wind speed (ffm), average relative humidity (RHm), maximum relative humidity (RHmax), and minimum relative humidity (RHmin). To assess the accuracy of the input parameters and models, the dew point (DP) was extracted from the testing data and evaluated using regression and tree-based models. Additionally, eight potential scenarios were defined to estimate the daily DP.

    Findings

    The study findings revealed that scenarios 1, 2, and 3 exhibited the highest correlation with dew point temperature, while scenarios 4, 5, 6, 7, and 8 displayed the lowest correlation. However, upon evaluating the scenarios based on the established criteria, it was determined that scenarios 1, 2, and 3 performed less effectively than the other scenarios. Consequently, the placement of the majority of parameters (scenarios 6, 7, and 8) led to a decrease in the models’ errors. The results obtained for the Gorgan models showed that the R-value ranged from 1 to 0.952. In the Gorgan station, the highest RMSE was observed for RT-3 at 2.0307, and the lowest RMSE was for SVR-8 at 0.222. Furthermore, the best fit for SVR-8 was characterized by RMSE: 0.222, NSE: 0.999, MBE: 0.092, MAE: 0.147, WI: 1, and SI: 0.017, while the worst fit was for RT-3 with RMSE: 2.307, NSE: 0.882, MBE: 0.875, MAE: 1.745, WI: 0.971, and SI: 0.179. The results for the Shahrekord models indicated that the R-value ranged between 0.996 and 0.615. In the Shahrekord station, the highest RMSE was observed for RT-2 at 4.952, and the lowest RMSE was for SVR-7 at 0.550. Additionally, the best fit for SVR-7 was characterized by RMSE: 0.550, NSE: 0.989, MBE: 0.374, MAE: 0.346, WI: 0.997, and SI: -0.15, while the worst fit was for RT-2 with RMSE: 4.957, NSE: 0.131, MBE: 1.43, MAE: 3.914, WI: 0.754, and SI: -1.352.

    Conclusion

    In this study, meteorological data were fitted using various modeling techniques, including Gaussian Process Regression (GPR), Linear Regression (LR), M5P, M5Rules, Random Forest (RF), Regression Tree (RT), and Support Vector Regression (SVR), to obtain the dew point temperature at the Gorgan and Shahrekord weather stations. The performance of these models was then evaluated under different scenarios, and the best-performing models were selected for estimating the dew point temperature. The results indicate that the estimation accuracy of the models using a single input parameter, such as minimum temperature (Tmin), was lower compared to the other models in both the Shahrekord and Gorgan stations. The SVR model with scenario 8 and the M5P model with scenario 7 demonstrated the best performance in estimating the dew point temperature for the Gorgan and Shahrekord stations, respectively. Furthermore, the comparison of the selected models revealed that the SVR model had the highest accuracy among the models evaluated. For the Gorgan station, the models were ranked from high to low accuracy as follows: SVR, M5P, M5Rules, GPR, RF, and LR. For the Shahrekord station, the ranking was M5P, M5Rules, GPR, RF, RT, and LR. Additionally, the comparison of the tree-based and regression-based models showed that the regression models, such as SVR and LR, had higher accuracy in estimating the dew point temperature compared to the tree-based models, such as M5P, M5Rules, and RT.

    Keywords: Dew Point Temperature, Gorgan, Regression Models, Shahrekord, Tree Models
  • Sara Namdar, Akram Abbaspour *, Ali Hosseinzadeh, Farzin Salmasi Pages 75-88
    Background and Objectives

    Weirs are the most important structures for measuring and regulating flow rate. They are also simple hydraulic structures used to control the water level and measure the flow rate in canals. Lateral weirs are sometimes known as side weirs. Side weirs are usually made in various geometric shapes such as rectangular, arched, trapezoidal and triangular. A complete analytical solution of the equations governing the side weir discharge is not possible as there are many parameters influencing the flow phenomenon. An accurate computation of lateral discharge mainly depends on the proper estimation of its discharge coefficient. Investigation of the discharge coefficient has been the main focus by many researchers. In trapezoidal side weirs, weir height, hydraulic head, and weir wall angle affect the discharge coefficient. Although many researchers have done studies on the theoretical and practical applications of simple side weirs, there are only limited investigations on compound sharp-crested side weirs. Recently, some experimental works have been done in order to understand flow hydraulics of compound sharp- and broad- crested normal weirs, which are built across the channel. In this study, the effect of the mentioned parameters on the discharge coefficient, hydraulic characteristics of the flow including flow rate, the water surface profile and energy variations were investigated numerically.

    Methodology

    Numerical simulation geometry which is composed of a rectangular channel and installed side weir, created and meshed using GAMBIT (ANSYS) . The experimental domain length is 2 m in order to avoid large mesh numbers. A quad-map mesh was generated for all models. In the present study, 3-dimensional numerical simulation of  flow over  trapezoidal sharp-crested side weir was evaluated using three turbulence models of  standard k-ε, RNG k-ε and  Realizable k-ε. The free surface was determined using the VOF method. The results showed that the RNG k-ε turbulence model and VOF method are suitable for predicting the discharge coefficient in trapezoidal plan side weirs. The studied models for trapezoidal side weirs were meshed using different node values to determine the optimum number of nodes to generate mesh and to perform a mesh independence test , a negligible difference was observed by increasing the number of nodes in simulated and measured the discharge coefficient of flow over trapezoidal side weir. Therefore, a mesh composed of approximately 25000 elements was considered as an optimum mesh for all created models to resolve flow characteristics. The boundary conditions were defined for all models. At the channel inlet and outlet, pressure inlet and outlet boundary conditions were used. For free surface, pressure inlet boundary condition was defined and wall boundary condition was assigned at the channel bed, side walls and the structure of weir. The comparison of the discharge coefficient of flow that data obtained from numerical simulation agreed well with the experimental data. Also results showed that VOF method can simulate free surface variations accurately enough given that the average relative error values of measured and simulated the discharge coefficient were 2 - 6% for all considered turbulence models. For sharp-crested side weirs in subcritical flow conditions, the equation  of  De Marchi (1934)was used to compute  the flow discharge coefficient of the side weirs.

    Finding

    In this study the water surface and flow patterns were analyzed using contour plots at different horizontal and vertical planes.  Using a non-linear regression model, they proposed a dimensionless relationship for prediction of the discharge coefficient in trapezoidal side weirs in subcritical flow conditions. In this study, the effect of weir wall angle, weir height and the hydraulic head on the discharge coefficient  of trapezoidal side weirs was investigated numerically using FLUENT software and also the numerical results were compared with the experimental data. The results of the simulation were in a good agreement with the experimental data. The discharge coefficient (Cm) is not dependent on any single hydraulic or geometric parameter, but several parameters affect it. The results also showed that the side weir with a wall slope of  z = 1 has better performance compared to the other two angles. Because it has the highest amount of the discharge coefficient among different weirs.

    Conclusion

    As a result of the flow passing through the side weir, the main channel's flow rate and longitudinal velocity decrease. It can be concluded that the velocity values near the side weir decrease with a greater slope, and the velocity variations decrease downstream in the main channel.

    Keywords: Discharge Coefficient, FLUENT Software, Trapezoidal Side Weir, Turbulence Models
  • Bagher Nikoufar *, Vahid Nourani Pages 89-110
    Background and Objectives

    Optimum operation of dam reservoirs is one of the most significant management factors in developing the annual resource and consumption plan of dam reservoirs during operation. The decisions regarding amount of water release are made by having the volume of  the reservoir, amount of demand, and the prediction of reservoir inflow in the actual operation of dam reservoirs. Since the volume of release is related to the storage volume of the reservoirs of the dams and should be optimized simultaneously, after introducing the genetic algorithm and the particle swarm algorithm, the performance of these algorithms alone and in combination with each other in the optimal operation of the Alavian dam reservoir are compared with the modeling results in the nonlinear programming and the rule curves of the operation are developed in this study. The performance indicators of the reservoir were been used including reliability, vulnerability and stability  to evaluate the performance of the examined algorithms in the optimal operation of the reservoir.

    Methodology

    In this study, after introducing the genetic algorithm and the particle swarm algorithm, innovatively examines the accuracy and effectiveness of modeling by comparing the performance of these algorithms both individually and in combination. This comparison focuses on optimizing the operation of the Alavian dam reservoir over multi-step ahead, using modeling results from the software Lingo. To enhance decision-making for improved management of the Alavian dam reservoir, operation rule curves have been developed. The model utilizes a series of 25 years of data from the Alavian dam, which includes the volume of inflow, the volume of release from the reservoir, storage volume, and usage data encompassing drinking, agriculture, industry, and environmental needs. Additionally, information such as the volume of overflow from the dam reservoir and the volume of evaporation from the surface of the Alaviyan Dam reservoir has been collected on a monthly basis.

    Findings

    The results from these optimal solutions indicate that the combined algorithm outperforms other methods, demonstrating a better correlation with the reservoir management policy. Over the last 25 years, the combined algorithm met 85% of the water requirements for agriculture downstream of Alavian dam, compared to 82% for the Particle swarm optimization(PSO)  algorithm and 78% for the genetic algorithm (GA). In contrast, the nonlinear programming (NLP) method met 80%. The total shortages over the entire 25-year operational period for the GA, PSO, GA-PSO, and NLP algorithms were 38, 33.7, 27.1, and 35.2 million cubic meters, respectively. The GA-PSO algorithm has successfully addressed 10.87 million cubic meters more than the GA algorithm and 6.57 million cubic meters more than the PSO algorithm.

    Conclusion

    Investigating the results obtained from the optimal solutions revealed that the hybrid algorithm model provides a more favorable result and shows a better correlation regarding the reservoir operation policy. The results indicate the high performance of the hybrid algorithm compared to other studied methods in the optimal operation of the single reservoir system of Alavian dam. Accordingly, the optimal parameters of the Alavian dam reservoir were obtained using a hybrid algorithm. It was proposed to  release volume rule curves and reservoir volume for the  multi-step ahead.

    Keywords: Particle Swarm Optimization Algorithm, Genetic Algorithm, Hybrid Algorithm, Optimal Operation, Rule Curve, Alavian Dam