Prediction of monthly rainfall using artificial neural network mixture approach, Case Study: Torbat-e Heydariyeh
محل انتشار: مجله فیزیک زمین و فضا، دوره: 44، شماره: 4
سال انتشار: 1397
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 72
فایل این مقاله در 12 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_JESPHYS-44-4_010
تاریخ نمایه سازی: 26 مهر 1402
چکیده مقاله:
Rainfall is one of the most important elements of water cycle used in evaluating climate conditions of each region. Long-term forecast of rainfall for arid and semi-arid regions is very important for managing and planning of water resources. To forecast appropriately, accurate data regarding humidity, temperature, pressure, wind speed etc. is required.This article is analytical and its database includes ۷۳۳۶ records situated in ۱۱ features from daily brainstorm data within a twenty year period. The samples were selected based on a case study in Torbat-e Heydariyeh. ۷۰% were chosen for learning and ۳۰% were chosen for taking tests. From ۷۱۸۱ available data, ۷۵% and ۲۵% were used for training and evaluating, respectively. This research studied the performance of different neural networks in order to predict precipitation and then presented an algorithm for combining neural networks with linear and nonlinear methods. After modeling and comparing their results using neural networks, the root mean square error was recorded for each method. In the first modeling, the artificial neural network error was ۰.۰۵, in the second modeling, linear combination of neural networks error was ۰.۰۷, and in the third model, nonlinear combination neural networks error was ۰.۰۰۱. Reducing the error of forecasting precipitation has always been one of the goals of the researchers. This study, with the forecast of precipitation by neural networks, suggested that the use of a more robust method called a nonlinear combination neural network can lead to improve men is in for cast diagnostic accuracy.
کلیدواژه ها:
نویسندگان
Iman Zabbah
Lecturer, Department of Computer, Torbat-e Heydariyeh branch, Islamic Azad University, Torbat-e Heydariyeh, Iran
Ali Reza Roshani
Assistant Professor, Department of Water Engineering, Torbat-e Heydariyeh branch, Islamic Azad University, Torbat-e Heydariyeh, Iran
Amin Khafage
M.Sc. Graduated, Department of Computer, Torbat-e Heydariyeh branch, Islamic Azad University, Torbat-e Heydariyeh, Iran
مراجع و منابع این مقاله:
لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :