مقایسه مدل‌های سری زمانی و شبکه عصبی با نتایج سناریوهای انتشار در پیش‌بینی بارندگی

نوع مقاله : مقالات پژوهشی

نویسندگان

• سحر بابایی حصار ¹
• رضا قضاوی ²

¹ دانشگاه سراسری کاشان

² دانشگاه کاشان

https://doi.org/10.22067/jsw.v0i0.32168

چکیده

بارش از مهمترین پارامترهای اقلیمی اثرگذار بر رژیم هیدرولوژیکی حوضه‌های آبخیز است. روش‌های مختلفی جهت پیش‌بینی میزان بارش ارائه شده است که از جمله آنها می‌توان به مدل‌‌‌های سری زمانی و شبکه عصبی مصنوعی اشاره نمود. این مدل‌ها بدون در نظر داشتن مسئله گرمایش جهانی و تغییر اقلیم پارامترهای اقلیمی را پیش‌بینی می‌کنند. هدف از انجام این مطالعه بررسی انطباق نتایج مدل‌های سری زمانی و شبکه عصبی مصنوعی با سناریوهای اقلیمی است. جهت انجام این مطالعه، ابتدا از میان مدل‌های مختلف سری زمانی بهترین مدل در برآورد متغیر بارندگی انتخاب گردید و با استفاده از 50 سال (1961 تا 2010) آمار بارندگی ایستگاه‌های سینوپتیک ارومیه تبریز و خوی، مقدار متغیر مذکور برای 18 سال آینده (2011 تا 2029) تولید شد. در گام بعد با استفاده از شبکه عصبی مصنوعی نیز مقدار بارندگی برای همان سال‌ها پیش‌بینی گردید. در نهایت نتایج این مدل‌ها، با داده‌های تولید شده تحت دو سناریوی B1 و A2 در مدل LARS-WG مقایسه شد. طبق نتایج بدست آمده معلوم شد شبکه عصبی مصنوعی تطابق بیشتری با مدل‌های جهانی اقلیم (GCM) دارد. مدل TS برخلاف سایر مدل‌های مورد استفاده یک روند نزولی برای بارندگی‌ پیش‌بینی کرده است.

کلیدواژه‌ها

• ایستگاه سینوپتیک
• بارندگی
• تغییر اقلیم
• مدل‌های اقلیمی
• LARS-WG

20.1001.1.20084757.1394.29.4.17.6

عنوان مقاله [English]

Comparison of TS and ANN Models with the Results of Emission Scenarios in Rainfall Prediction

نویسندگان [English]

• S. Babaei Hessar ¹
• R. Ghazavi ²

¹ Kashan University

² Kashan University

چکیده [English]

Introduction: Precipitation is one of the most important and sensitive parameters of the tropical climate that influence the catchments hydrological regime. The prediction of rainfall is vital for strategic planning and water resources management. Despite its importance, statistical rainfall forecasting, especially for long-term, has been proven to be a great challenge due to the dynamic nature of climate phenomena and random fluctuations involved in the process. Various methods, such as time series and artificial neural network models, have been proposed to predict the level of rainfall. But there is not enough attention to global warming and climate change issues. The main aim of this study is to investigate the conformity of artificial neural network and time series models with climate scenarios.
Materials and Methods: For this study, 50 years of daily rainfall data (1961 to 2010) of the synoptic station of Urmia, Tabriz and Khoy was investigated. Data was obtained from Meteorological Organization of Iran. In the present study, the results of two Artificial Neural Network (ANN) and Time Seri (TS) methods were compared with the result of the Emission Scenarios (A2 & B1). HadCM3 model in LARS-WG software was used to generate rainfall for the next 18 years (2011-2029). The results of models were compared with climate scenarios over the next 18 years in the three synoptic stations located in the basin of the Lake Urmia. At the first stage, the best model of time series method was selected. The precipitation was estimated for the next 18 years using these models. For the same period, precipitation was forecast using artificial neural networks. Finally, the results of two models were compared with data generated under two scenarios (B1 and A2) in LARS-WG.
Results and Discussion: Different order of AR, MA and ARMA was examined to select the best model of TS The results show that AR(1) was suitable for Tabriz and Khoy stations .In the Urmia station MA(1) was the best performance. Multiple Layer Perceptron with a 10 neurons in hidden layer and the output layer consists of five neurons had the lowest MSE and the highest correlation coefficient in modeling the values of annual precipitation. So MLP was determined as the best structure of neural network for rainfall prediction. According to results, precipitation predicted by the ANN model was very close to the results of A2 and B1 scenario, whereas TS has a significant difference with these scenarios. Average rainfall predicted by two A2 and B1 scenarios in Urmia station has more difference than other stations. Based on the B1 scenario, precipitation will increase 11 percent over the next two decades. It will decrease 10.7 percent according to A2 emissions scenario. According to ANN models and two A2 and B1 scenarios, the rates of rainfall will increase in Tabriz and Khoy stations. However, according to TS model, rainfall will decline 5.94 and 3.63 percent for these two stations, respectively.
Conclusion: Global warming and climate change should have adverse effects on groundwater and surface water resources. Different models are used for simulating of thes effects. But, conformity of these models with the results of climate scenarios is an issue that has not been addressed. In the present research coincidence of TS model, ANN model and climate change scenarios was investigated. Results show under emissions scenarios, during the next two decades in Tabriz and Khoy stations, precipitation will increase. In Urmia station B1 and A2 scenario percent increase by 11 percent and 10.5 percent decline predicted, respectively. The results of Roshan and et al (4) and Golmohammad and et al, (7) investigations show increasing trend in the rainfall rate and confirming the results of this study According to results, the performance of ANN model is better than TS model for rainfall prediction and its result is similar to climate change scenarios. Similar results have been reported by Wang et al (29) and the Norani et al (20). Due to the significant difference between the TS and climate scenarios used in the study area, is not recommended, though it can be used as a plausible climate scenario to predict the precipitation of stations in the future studied. At the end, it is suggested that the similar studies carried out in a larger number of stations in the country with respect to global warming and climate change, to determine the validity of the methods used to the predicted rainfall.

کلیدواژه‌ها [English]

• Climate change
• Emission Scenario
• Neural Network
• LARS-WG
• Time Seri

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