کاربست روش شبکه‌های عصبی در پیش‌بینی دمای سطح زمین، با استفاده از تصاویر حرارتی مادیس

نوع مقاله : علمی - پژوهشی

نویسندگان

1 استادیار مؤسسۀ ژئوفیزیک، دانشگاه تهران

2 استادیار دانشکده مهندسی صنایع و سیستم‌های مدیریت، دانشگاه صنعتی امیرکبیر، تهران

3 دانشجوی کارشناسی‌ارشد مهندسی دانش و علوم تصمیم، دانشگاه علوم اقتصادی، تهران

چکیده

در این مطالعه، مدلی ترکیبی از شبکه‌های عصبی ماژولار و پردازش تصاویر مادیس برای محاسبۀ دمای سطح زمین، در منطقه‌ای شامل شهر تهران، ارائه شده است. در این مدل، داده‌های تصاویر حرارتی با تکیه بر ویژگی‌های دمای درخشندگی در باندهای حرارتی 31 و 32 میکرومترسنجندۀ مادیس، به‌منزلۀ ورودی در شبکه‎های عصبی ماژولار به‌کار رفته و روش جدیدی براساس ترکیبی از شبکۀ عصبی نگاشت خودسازمانده و الگوریتم بهینه‌سازی تجمع ذرات پیشنهاد شد. نتایج به‌دست‌آمده نشان می‌دهد استفاده از این الگوریتم سبب توزیع مناسب داده‌های ورودی شبکه‌های عصبی می‌شود. در آخر، نتایج نهایی با مدل‌های شبکه‌های عصبی با آموزش و ساختار غیرماژولار نیز مقایسه شده است. نتایج این مقایسه نشان می‌دهد که زمان آموزش مدل در پیش‌بینی دمای سطح زمین کاهش، و دقت مدل افزایش یافته است. اختلاف کم بین مقادیر پیش‎بینی‌شده و مقادیر واقعی دما در منطقه نشان می‌دهد که دما با دقت مناسبی در این مدل پیش‌بینی شده است، به‌طوری‌که میانگین خطای مدل ترکیبی مقدار 0081/0 و درصد خطای مطلق نیز 59/10 است. 

کلیدواژه‌ها

عنوان مقاله [English]

Application of Neural Networks for Land Surface Temperature Forecasting Using MODIS Images

نویسندگان [English]

  • F Taghavi 1
  • A Ahmadi 2
  • Z Zargaran 3
1 Assistant Prof., Institute of Geophysics, University of Tehran
2 Assistant prof., Institute of Industrial Engineering and Management Systems, Amirkabir University of Technology
3 M.Sc. Student, University of Economic Sciences
چکیده [English]

In this study, a combined model of modular networks and satellite image processing and optimization algorithms to forecast land surface temperature in  an area  including city of Tehran is presented. Calculating the LST has been done based on brightness temperature features in 31 and 32 MODIS channels. Thus, brightness temperature data related to these images is fed to neural network and values of land surface temperature are recovered as the output of the network. In this way,after obtaining the optimal structure obtained for networks they are trained and their weights are extracted. Then by applying a neural network with a modular structure and clustering algorithms, training will be also modular. Decomposition of the networks and after that  combining the results to get the final forecast  makes the performance of the modular network more effective. As a result , a new approach based on the combination of neural network or self-organizing map and particle swarm optimization algorithms is proposed. The results showed that using PSO algorithm causes appropriate distribution of cluster of SOM method and using satellite images improved performance of the proposed model. Finally, results are compared with training neural network models and non-modular structure. The results of this comparison show that model-training time in predicting the land surface temperature is decreased and the accuracy of model increased. The little difference between the predicted values and actual (real) values of temperature in the region shows that this model could predict the temperature accuraetly, so that, in this hybrid model Mean Square Errors (MSE) and Mean Absolute Percentage Error (MAPE) are 0.0081 and 10.59 respectively.

کلیدواژه‌ها [English]

  • MODIS Images
  • Modular Neural Networks
  • Surface temperature

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