ارزيابي مدل‌هاي استوکستيکي در پيش‌بيني سطح آب زيرزميني دشت همدان ـ بهار
محورهای موضوعی : مدیریت بهینه منابع آب و خاکحامد نوذری 1 , نادیا صدق نژاد 2 , سجاد پویان فر 3
1 - دانشيار، گروه علوم و مهندسي آب، دانشکده کشاورزي، دانشگاه بوعلي سينا، همدان.
2 - دانشجوي دکتري، گروه علوم و مهندسي آب، دانشکده کشاورزي، دانشگاه بوعلي سينا، همدان.
3 - کارشناسي ارشد، گروه علوم و مهندسي آب، دانشکده کشاورزي، دانشگاه بوعلي سينا، همدان.
کلید واژه: هوش مصنوعي, مدل ماشين بردار پشتيبان (SVM), الگوريتم تبريد شبيه‌سازي (SA), ARIMA, SARIMA,
چکیده مقاله :
زمينه و هدف: ذخاير آب زيرزميني از مهم‌ترين منابع قابل دسترس آب شيرين در جهان محسوب مي‌شوند. امروزه با توجه به تغييرات آب و هوا، تغيير اقليم، افزايش جمعيت و برداشت‌هاي بي‌رويه از آب‌هاي زيرزميني، اين منابع با کاهش قابل توجهي مواجه شده است. با توجه به اينکه ايران در منطقه خشک و نيمه‌خشک واقع شده است، در بسياري از مناطق سطح آب زيرزميني دستخوش تغييرات بسياري قرار گرفته است. سطح آب زيرزميني نيز در منطقه دشت همدان-بهار با کاهش قابل توجهي مواجه شده است. از اين رو پيش‌بيني مقادير سطح آب زيرزميني در دشت همدان-بهار و مديريت عوامل موثر در کاهش آن از اهداف اساسي اين پژوهش مي‌باشد.
روش پژوهش: در گام نخست، در اين پژوهش سعي شد به کمک مدل تلفيقي ماشين بردار پشتيبان با الگوريتم تبريد شبيه‌سازي (SVM-SA) سطح آب زيرزميني با استفاده از مقادير بارش 4 ايستگاه سينوپتيک آق‌کهريز، سد اکباتان، کوشک آباد و مريانج برآورد گرديد و عدم قطعيت مدل SVM-SA نيز مورد تجزيه و تحليل قرار گرفت. در گام بعد، مقادير بارش 4 ايستگاه سينوپتيک مذکور به مدت 5 سال بصورت ماهانه و سالانه به کمک مدل‌هاي ميانگين متحرک يکپارچه خودهمبسته فصلي (SARIMA) و ميانگين متحرک يکپارچه خودهمبسته (ARIMA) مورد پيش‌بيني قرار گرفت و در نهايت با استفاده از مقادير بارش پيش‌بيني شده، سطح آب زيرزميني با استفاده از مدل SVM-SA به مدت 5 سال به‌صورت ماهانه و سالانه مورد پيش‌بيني قرار گرفت.
يافته‌ها: مقادير برآورد سطح آب زيرميني به کمک مدل SVM-SA با استفاده از شاخص‌هاي ضريب تبيين (R2)، جذر ميانگين مربعات خطا (RMSE) و ضريب نش ساتکليف (NSE) مورد بررسي قرار گرفت. نتايج نشان داد که عملکرد مدل در پيش‌بيني سطح آب زيرزميني در دوره‌هاي سالانه و ماهانه مناسب بوده و اختلاف معني داري در نتايج پيش‌بيني ماهانه و سالانه وجود ندارد. اما مدل SVM-SA با ضريب نش ساتکليف 0.993، جذر ميانگين خطاي استاندارد 0.417 و ضريب تبيين 0.993 در دوره واسنجي در برآورد ماهانه سطح آب زيرزميني کمي دقت بالاتري داشته است. در گام بعد جهت دستيابي به بهترين مرتبه مدل SARIMA و ARIMA جهت پيش‌بيني مقادير بارش ماهانه و سالانه از شاخص‌هاي آماري ضريب تبيين (R2)، جذر ميانگين مربعات خطا (RMSE)، ميانگين خطاي استاندارد (SE) و سنجش نيکويي برازش (AIC) استفاده شد. در نهايت با استفاده از مرتبه‌هاي مدل‌هاي منتخب با توجه به شاخص‌هاي ارزيابي براي دوره‌هاي ماهانه و سالانه براي استگاه‌هاي آق‌کهريز به‌ترتيب SARIMA(3,0,1)*(1,0,1) و ARIMA(3,0,2)، ايستگاه سد اکباتان به‌ترتيب SARIMA(1,0,1)*(1,1,2) و ARIMA(3,1,3)، براي ايستگاه کوشک آباد به‌ترتيب SARIMA(1,1,3)*(1,1,1) و ARIMA(2,0,3) و براي ايستگاه مريانج به‌ترتيب SARIMA(1,0,1)*(1,1,2) و ARIMA(3,0,2)، مقادير بارش براي 5 سال بصورت ماهانه و سالانه مورد پيش‌بيني قرار گرفت. در نهايت با استفاده از مقادير بارش پيش‌بيني شده به کمک مدل‌هاي SARIMA و ARIMA، سطح اب زيرزميني با استفاده از مدل SVM-SA بصورت ماهانه و سالانه براي 5 سال آينده مورد پيش‌بيني قرار گرفت.
نتيجه‌گيري: از نتايج مهم اين مطالعه مي‌توان به اين نکته اشاره کرد که رابطه معني‌داري بين کاهش بارش و افت شديد آب‌هاي زيرزميني در دشت همدان-بهار وجود ندارد. در واقع نتايج اين پژوهش حاکي از آن است که افت شديد سطح آب زيرزميني در سال‌هاي اخير ناشي از برداشت بي‌رويه از اين منابع ارزشمند مي‌باشد.
Background and Aim: Groundwater sources are considered to be one of the most important available sources of fresh water in the world. Today, due to the changes in weather, climate change, population increase and excessive withdrawals of underground water, these resources have faced a significant decrease. Considering that Iran is located in a dry and semi-arid region, the underground water level has undergone many changes in many areas. The level of underground water in Hamadan-Bahar plain area has also faced a significant decrease. Therefore, the prediction of underground water levels in Hamadan-Bahar plain and the management of effective factors in its reduction are the main goals of this research.
Method: In the first step, in this research, it was tried to predict the underground water level with the help of support vector machine integrated model with Simulated annealing algorithm (SVM-SA) using the rainfall values of 4 synoptic stations of Aghkahriz, Ekbatan Dam, Kooshkabad and Marianaj. The uncertainties of this model are also analyzed. In the next step, the precipitation values of the mentioned 4 synoptic stations were predicted for 5 years monthly and annually with the help of seasonal autocorrelated moving average (SARIMA) and autocorrelated moving average (ARIMA) models, and finally, using the predicted rainfall values, the underground water level was predicted monthly and annually using the SVM-SA model for 5 years.
Result: The estimated values of underground water level were analyzed with the help of SVM-SA model using the indices of explanation coefficient (R2), root mean square error (RMSE) and Nash Sutcliffe coefficient (NSE). The results indicate that there is no significant difference between the performance of the model in predicting the underground water level in annual and monthly periods. But the SVM-SA model with Nash Sutcliffe coefficient of 0.993, root mean standard error of 0.417 and explanatory coefficient of 0.993 in the calibration period has been more accurate in monthly estimation of underground water level. In the next step, in order to achieve the best SARIMA and ARIMA models for predicting monthly and annual rainfall values, statistical indicators of coefficient of explanation (R2), root mean square error (RMSE), mean standard error (SE) and goodness of fit (AIC) are used. Finally, by using the ranks of the selected models according to the evaluation indices for monthly and annual periods for Aghkahriz station, respectively SARIMA(3,0,1)*(1,0,1) and ARIMA(3,0,2), for Ekbatan dam station according to SARIMA(1,0,1)*(1,1,2) and ARIMA(3,1,3), for Kooshkabad station according to SARIMA(1,1,3)*(1,1,1) and ARIMA(2,0,3) and for Marianaj station by SARIMA(1,0,1)*(1,1,2) and ARIMA(3,0,2) respectively, rainfall values for 5 years in monthly and annually forecast it placed. Finally, using the forecasted rainfall values with the help of SARIMA and ARIMA models, the groundwater level was forecasted monthly and annually for the next 5 years using the SVM-SA model.
Conclusion: One of the important results of this study is the absence of a significant relationship between the decrease in rainfall and the sharp drop in groundwater in the Hamedan-Bahar plain. In fact, the results of this research indicate that the sharp drop in the underground water level is caused by the excessive extraction of these valuable resources.
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