پیش بینی خشکسالی هواشناسی، هیدرولوژیکی و کشاورزی مبتنی بر روش موجک در تهران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش آموخته کارشناسی ارشد رشته مهندسی و مدیریت منابع آب/ واحد اسلامشهر، دانشگاه آزاد اسلامی، اسلامشهر، ایران

2 گروه مهندسی عمران/ واحد اسلامشهر، دانشگاه آزاد اسلامی، اسلامشهر، ایران

چکیده

 
پیش‌بینی خشکسالی نه تنها یکی از چالش­های اساسی کشور بلکه چالشی برای کشورهای دارای فناوری مدلسازی دینامیکی پیش‌بینی ماهانه است. با توجه به خسارت­های سنگینی که توسط بروز پدیده­ی خشکسالی به بشر تحمیل می­گردد، پیش­بینی هرچه دقیق­تر خشکسالی از اهمیت ویژه­ای برخوردار است. روش تلفیقی موجک- شبکه­عصبی یکی از روش­های بسیار دقیق برای این مهم می­باشد. یک عامل مهم و تاثیر­گذار در نتایج تبدیلات موجک، استفاده از موجک مادر مناسب می­باشد. هدف از این پژوهش تعیین موجک مادر بهینه برای پیش­بینی دقیق­تر انواع خشکسالی می­باشد. بدین منظور به ترتیب از داده­های بارش روزانه، دبی روزانه و تصاویر ماهواره­ای مربوط به شهر تهران از سال 1969 تا سال 2016 به عنوان داده­های خام برای محاسبه­ی سری زمانی خشکسالی هواشناسی، هیدرولوژیکی و کشاورزی استفاده گردید. برای پیش­بینی خشکسالی­ها از روش تبدیلات موجک WT1 و شبکه عصبی PNN2 توامان استفاده شد. از هر سری زمانی خشکسالی تبدیلات موجک با استفاده از موجک haar و bior1.1 گرفته شد و پیش­بینی توسط شبکه­ی عصبی انجام پذیرفت. نتایج پیش­بینی با استفاده از موجک مادر haar نشان داد ضریب همبستگی برای خشکسالی­های هواشناسی، هیدرولوژیکی و کشاورزی به ترتیب 68039/0، 76271/0 و 92697/0 می­باشد. سپس، پیش­بینی هر نوع خشکسالی با موجک مادر bior1.1 انجام پذیرفت و مشخص شد ضریب همبستگی برای خشکسالی هواشناسی، هیدرولوژیکی و کشاورزی  به ترتیب 71169/0، 74147/0 و 82049/0 می­باشد. نتایج کلی نشان داد که موجک مادر bior1.1 برای پیش‌بینی خشکسالی هواشناسی بهتر ظاهر شده و نتایج بهتری را در اختیار ما قرار می­دهد. اما در خصوص پیش­بینی دو نوع خشکسالی هیدرولوژیکی و کشاورزی موجک مادر haar نتایج بهتری در اختیار ما قرار می­دهد.  

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Meteorological, Hhydrological And Agricultural Droughts Prediction Using Wavelet Method In Tehran

نویسندگان [English]

  • MohammadHosein Mashayekhi 1
  • Mahmoud Zakeri Niri 2
1 Water Resources Management and Engineering Graduated, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran
2 Department of Civil Engineering, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran
چکیده [English]

According to the heavy damage caused to humans by the occurrence of drought, it is important to predict the drought as accurately as possible.The wavelet-neural network integration method is one of the most accurate methods for this important.There are several categories of drought, including meteorological,hydrological,and agricultural drought.In this study,authors was tried to determine the optimal wavelet for predicting different types of drought.daily precipitation data,daily discharge,and satellite imageryrelated to Tehran from 1969 to 2016 were use as raw data to calculate the indicators.The wave transformations (WT) conversion method and PNN neural network have been used to predict droughts.From each time series of drought, wavelet transformations were performed using haar and bior1.1waves,and the prediction was make by neural network.It was found that the regression coefficient and error concentration for meteorological drought using haar wave are 0.68and 0.033and regression coefficient and error concentration for hydrological drought using haar wave are 0.76and0.066and regression coefficient and error concentration for agricultural drought using haar wave are 0.9269and0.1515.Then, the prediction of any kind of drought was done with bior1.1wave and it was found that the regression coefficient and error concentration for meteorological drought using bior1.1wave are 0.7116and 0.992.and the regression and concentration coefficientThe error for hydrological drought using bior1.1wave is0.14147and 0.0329and the regression and error concentration coefficient for agricultural drought using bior1.1 wave are0.82049and0.0016.The results showed that for meteorological drought, the bior1.1wave appeared better and gave us better results.But in two types of hydrological and agricultural droughts,it was found that the haar wave gives us better results.

کلیدواژه‌ها [English]

  • Wavelet Transform (WT). standardized runoff index (SRI)
  • Normalized Difference Vegetation Index(NDVI)
  • Haar Wave Bior1.1 Wave
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