نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری

2 دانشیار دانشگاه تربیت مدرس

3 استادیار دانشکده نقشه برداری دانشگاه صنعتی خواجه نصیرالدین طوسی

4 استادیار دانشگاه تربیت مدرس

چکیده

در سال‌های اخیر پدیده خشکسالی خسارت‌های فراوانی به بخش کشاورزی کشور وارد آورده که وجود یک سیستم پیش آگاهی از تأثیر آنبر محصولات کشاورزی را برای کمک به سیاستگذاران و ذی­نفعان ضروری­می­سازد.  در این تحقیق، مدلی برای ارزیابی پیش­بینی آسیب ناشی از خشکسالی کشاورزی برای استان کرمانشاه با استفاده از روش‌های آماری و هوشمند توسعه یافته است.  این مدل به­طور خاص برای محصول گندم دیم است و می­تواند خود را همراه با رشد گیاه و در مراحل مختلف فنولوژیک بهنگام کند.  در فرایند توسعة مدل، از شاخص‌های خشکسالی PDSI، Z-index، CMI، SPI و EDI استفاده و جهت انتخاب متغیرهای مناسب، روش­های الگوریتم ژنتیک، و شبکة مصنوعی عصبی به­کار گرفته شد.  نتایج نشان می­دهد که شاخص Z-index نسبت به بقیه شاخص‌ها آسیب ممکن را بهتر پیش­بینی می‌کند.  همچنین، مدل با گذشت زمان در مراحل مختلف بحرانی رشد از برازش بهتری برخوردار می­شود و به­خصوص از مرحلة سوم به بعد، سطح معنی­داری روابط به 1 درصد رسیده و نتایج پیش­بینی قابل اتکا می­شود.  همچنین، اتصال مدل به محیط سامانه اطلاعات جغرافیایی قابلیت­های آن را برای تحلیل­های لازم مکانی و ارائة کارآمدتر نتایج ارتقاء نمود. 

عنوان مقاله [English]

Agricultural Drought Risk Assessment Model for Kermanshah Province, Using Statistical and Intelligent Methods

چکیده [English]

The agriculture sector has been affected by severe drought in recent years, making development of a drought warning system for agriculture crucial. Such a system can be a useful tool for policy makers and investors. This research develops a model for agricultural drought risk assessment using statistical and intelligent methods. Kermanshah province, a major rain-fed region of Iran, was selected as the study area. The model is specific to rain-fed wheat and was updated during the different phonological stages of the growing season. The inputs are a combination of the PDSI, Z-index, CMI, SPI and EDI drought indices which were selected using genetic algorithm and artificial neural networking techniques. The results show that the Z-index better predicts possible losses. The general performance of the model increased toward the end of the growing season, especially after the third stage, when the significance level of the relation reaches 1% and the results become more reliable. Furthermore, linkage of the model to the geographical information system makes it more capable of spatial analysis and more suitable for presentation of the final results.

کلیدواژه‌ها [English]

  • Agricultural drought
  • Genetic algorithm
  • Kermanshah Province
  • Principle Component Analysis
  • Vulnerability Assessment
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