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ارزیابی کارایی چندین روش دادهکاوی برای پیشبینی تبخیر(مطالعة موردی: ایستگاه سینوپتیک یزد) | ||
نشریه علمی - پژوهشی مرتع و آبخیزداری | ||
مقاله 2، دوره 71، شماره 3، آذر 1397، صفحه 579-594 اصل مقاله (1.25 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/jrwm.2018.31403.567 | ||
نویسندگان | ||
حمیده افخمی* 1؛ اعظم حبیبی پور1؛ محمد رضا اختصاصی2 | ||
1دکتری علوم و مهندسی آبخیزداری، دانشکدة منابع طبعی و کویرشناسی، دانشگاه یزد | ||
2استاد دانشکدة منابع طبعی و کویرشناسی، دانشگاه یزد | ||
چکیده | ||
تبخیر یکی از پارامترهای اقلیمی مهم در مناطق خشک است و نقش مهمی را در مدیریت منابع آب بازی میکند، به همین جهت آگاهی از مقدار تبخیر و مدلسازی آن به عنوان یکی از متغیرهای مهم هیدرولوژیکی در تحقیقات کشاورزی و حفاظت آب و خاک از اهمیت زیادی برخوردار است. در دهههای اخیر روشهای هوش مصنوعی در تخمین و پیشبینی پدیدههای غیرخطی توانایی بالایی از خود نشان داده است. در این تحقیق از سه روش مهم دادهکاوی شامل شبکة عصبی مصنوعی، شبکههای استنتاج فازی و درخت تصمیم رگرسیونی جهت پیشبینی تبخیر ماهانه در ایستگاه سینوپتیک یزد استفاده شد. برای این منظور از 8 متغیر هواشناسی در مقیاس ماهانه (متوسط کمینة دما، متوسط بیشینة دما، میانگین دما، ساعات آفتابی، سرعت باد، جهت باد، میانگین رطوبت نسبی و تبخیر) به عنوان ورودی مدل استفاده گردید. نتایج بهدستآمده نشان داد هر سه مدل نامبرده قادرند با استفاده از پارامترهای اقلیمی مذکور به پیشبینی مقدار تبخیر ماهانه 12 ماه بعد از وقوع بپردازند ولی در میان سه مدل مورد استفاده، شبکة عصبی مصنوعی با ضریب همبستگی برابر با 97/0r=، 1/5RMSE=،3/36MAE= و 48/0-ME= بهترین کارایی را از خود نشان داد. همچنین نتایج نشان داد در پیشبینی تبخیر، تفاوت قابلملاحظهای در زمان استفاده از دادههای خام و دادههای نرمال شده وجود ندارد و پردازش دادهها تأثیر چندانی در بهبود نتایج مدلها نخواهد داشت. | ||
کلیدواژهها | ||
پیشبینی؛ تبخیر؛ شبکة عصبی مصنوعی؛ استنتاج فازی؛ درخت تصمیمگیری؛ یزد | ||
عنوان مقاله [English] | ||
Performance assessment of data mining techniques for Forecast for one year evaporation (A Case Study: Yazd synoptic station) | ||
نویسندگان [English] | ||
hamide afkhami1؛ azam habibi pour1؛ mohammad reza ekhtesasi2 | ||
1student of yazd university | ||
2yazd | ||
چکیده [English] | ||
Evaporation is considered one of the key climatic variables, especially in arid regions and evaporation losses is one of the important issues in irrigation and water resources management in these areas. Therefore, it is important being aware of the amount of evaporation and its modeling, as one of the most important hydrological variables in agricultural research and water and soil conservation. In recent decades, artificial intelligence techniques have proven high capability and flexibility to estimate and predict nonlinear phenomena. In this study, three important data mining techniques including Artificial Neural Network, Active Neuro-Fuzzy Inference System and Regression Decision Tree were used for predicting evaporation. For this purpose, 8 climatic variables (Minimum average temperature, average maximum temperature, average temperature, sunshine hours, wind speed, wind direction, relative humidity and evaporation averages) were employed in this study. The results showed three models are able to predict evaporation for 12 months after. Finally among the used models, ANN showed better performance with coefficient efficiency of 0.97 and RMSE of 5.1and ME of 0.48. Also, The results showed that there is not significant difference in simulation results to predict the evaporation between two scenario, original data and normalized data. | ||
کلیدواژهها [English] | ||
Prediction, Evaporation, Artificial Neural Network, Active Neuro-Fuzzy Inference System, Decision Tree, Yazd | ||
مراجع | ||
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