مدل‌سازی بارش- رواناب ایستگاه‌های هیدرومتری خرمازرد و بناب با استفاده از الگوریتم ماشین بردار پشتیبان و جنگل تصادفی

شبیه‌سازی فرآیند بارش-رواناب می‌تواند نقش بسزایی در مدیریت منابع آب و مسائل هیدرولوژی داشته باشد. در این تحقیق با استفاده از مدل‌های داده‌کاوی ماشین بردار پشتیبان (svm) و جنگل تصادفی (rf) اقدام به مدل‌سازی بارش رواناب دو ایستگاه بناب و خرمازرد به‌ترتیب واقع بر روی رودخانه‌های صوفی‌چای و ماهپری‌چای (دشت مراغه) شده است. در مطالعه حاضر داده‌های ایستگاه‌های هواشناسی و هیدرومتری منطقه از سال 1355 تا 1397 از شرکت آب منطقه‌ای و سازمان هواشناسی استان آذربایجان شرقی دریافت گردید. تغییر روند رواناب جاری در سال 1374، باعث گردید مدت مطالعه به دو دوره قبل و بعد آن تقسیم شود. مقدار بارش و رواناب با تاخیر زمانی یک ماه بعنوان ورودی به این مدل وارد و سپس مقادیر رواناب ماهانه مشاهداتی با رواناب ماهانه تخمین زده شده با استفاده از معیارهای ارزیابی خطا مورد بررسی گرفت. نتایج نشان داد که در هر دو دوره برای ایستگاه بناب مدل svm کارآیی بالاتری نسبت به مدل rf داشت و در ایستگاه خرمازرد نیز برای این دو دوره، مدل rf عملکرد بهتری از مدل svm ارائه کرد. نتایج مدل‌سازی در مجموعه تست در دو ایستگاه نشان داد که مقدار همبستگی متقابل برای دو دوره مطالعاتی اول و دوم ایستگاه بناب ‌به‌ترتیب برابر با 0.85 و 0.84 و برای ایستگاه خرمازرد برابر با 0.79 و 0.75 بدست آمد. با توجه به نتایج مقادیر آماره من کندال و سری‌های زمانی برای هر دو ایستگاه، روند مشخصی برای بارش در طول دوره مشاهده نشد، ولی دبی رودخانه‌ صوفی‌چای در ایستگاه بناب، بخصوص بعد از سال 1374 روند صعودی و دبی رودخانه ماهپری‌چای روند کاملا نزولی داشته است.

rainfall-runoff modeling of khormazard and bonab hydrometric stations using support vector machine and random forest algorithms

introductionwater plays a crucial role in ensuring the sustainable development of any region. given that our country consists primarily of arid and semi-arid regions, where the majority of rivers are also found, along with the critical state of groundwater extraction and the growing importance of surface water, it is crucial to have a deep understanding of the future condition of water resources within the country’s watersheds (fathollahi et al., 2015). by utilizing intelligent models, it becomes feasible to represent the inherent relationships between data that cannot be solved by conventional mathematical methods. support vector machine (svm) and random forest algorithms are two types of machine learning methods that utilize essential algorithms for making repeated and accurate predictions (kisi parmarm, 2016). the most recent study conducted by zarei et al. (2022) evaluated the risk of flooding using data mining models of svm and rf (case study: frizi watershed). by analyzing the results, it was found that both the svm algorithm and the new random forest algorithm showed higher accuracy in predicting flooding risks, both in terms of the educational data and algorithmic performance. the purpose of this study is to simulate the precipitation-runoff process in the hydrometric stations at the end of the maragheh plain (khormazard station on the mahpari chai river and bonab station on the sufichai river) in east azerbaijan province using support vector machine and random forest modeling algorithms. this study has been conducted over a period of 43 years, making it one of the few research cases in this area. materials and methodsthe maragheh sufi chai basin is situated in the eastern region of lake urmia, within the east azarbaijan province. it covers an area of 611.89 square kilometers and is located between longitudes 45° and 40´ to 46° and 25´and latitudes from 37° and 15´ to 37° and 55´ north. the average height of the basin is 1767 meters above sea level (sharmod et al., 2015). based on the substantial changes observed in the runoff trend in the data since 1994 (without any noticeable change in the precipitation trend), the available data was divided into two distinct periods. the first period spans from 1976 to 1994, and the second period covers the years 1995 to 2019. to simulate rainfall-runoff, first the average rainfall of maragheh plain was calculated by polygonal method. subsequently, this data was combined with the discharge output from bonab and khormazard stations, with a one-day time lag. these inputs were then utilized in two models, svm (kernel function) and rf. for this purpose, 70% of the data was used for the training stage and 30% of the data was used for the validation stage. then, the rainfall and runoff training sets from one day before were chosen as the predictor variables, while the runoff training set was designated as the target variable. several combinations of runoff and rainfall inputs were evaluated for the purpose of modeling. the inputs consist of the monthly q and p values that were recorded previously (pt, qt-1), while the output represents the current runoff data (qt), with the subscript t indicating the time step. as a result, two input combinations were constructed from q and p data (as seen in table 3) and svm and rf models were used for rainfall-runoff modeling to determine the optimal input combination.calculating average rainfall through the thiessen polygons methodthiessen polygons, which are voronoi cells, are used to define rainfall polygons that correspond to the surface area (ai). these polygons are used to weight the rainfall measured by each rain gauge (ri). consequently, the area-weighted rainfall is equivalent to:                                                  (1)random forest algorithmrandom forest is a modern type of tree-based methods that includes a multitude of classification and regression trees. this algorithm is one of the most widely used machine learning algorithms due to its simplicity and usability for both classification and regression tasks.support vector machine (svm) algorithmsupport vector machines works like other artificial intelligence methods based on data mining algorithm. the most important functions of the support vector machine model are classification and linearization or data regression.       evaluation criteriato evaluate the models and compare their effectiveness, this research employs metrics such as the root mean square error (rmse), correlation coefficient (r), explanation coefficient (r2) and nash-sutcliffe efficiency coefficient (ns) are used. below are the relationships among these criteria:   (2)            (3)                                                         (4) (5)    results and discussion figure 6 displays the time series data for rainfall and runoff during the two study periods, before and after 1994.the analysis of the figures showed that for bonab station, during the two study periods, the value of kendall’s statistic for precipitation variable was 0.044 and 0.028, respectively. for khormazard station, this statistic value for the first and second period was 0.030, and 0.028, respectively. however, these values are not significant at the 95% level. this indicates that the annual rainfall for the two studied stations during these years is not statistically significant. therefore, it is concluded that the annual rainfall in these stations between the years 1976 to 2019 did not show any significant trend. the variations observed during this period were deemed normal, suggesting that the time series of rainfall displayed fluctuating patterns. however, it should be noted that there were instances of both increasing and decreasing trends in certain years examining the time series reveals varying trends initially, the outflow from bonab station (both a and b) displayed fluctuating patterns, followed by periods of both decreasing and increasing trends. however, in recent years, there has an increase in outflow from this station. the mann-kendall test statistic for the two study periods for this station is 0.325 and 0.512, respectively. these values are significantly different at the 95% level, indicating that the increasing trend of discharge for both time periods was statistically significant. the reason for this trend at the bonab station, compared to other entrance stations to lake urmia, is the lower demand for water in the sofichai basin for agricultural and industrial purposes, in contrast to other rivers. to explore the root cause of this issue, studies should be conducted to examine both underground and surface