Modeling and predicting the time series of drought indices ssing machine learning methods in order to manage hazards (Case study: Eastern district of Isfahan)

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KHOSRAVI IMAN; AKHONDZADEH MEHDI; Khoshgoftaar Mohammad Mehdi

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Journal: ENVIRONMENTAL HAZARDS MANAGEMENT Year:2015 | Volume:2 | Issue:1 Page(s): 51-65

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Title

Modeling and predicting the time series of drought indices ssing machine learning methods in order to manage hazards (Case study: Eastern district of Isfahan)

Author(s)

KHOSRAVI IMAN | AKHONDZADEH MEHDI | Khoshgoftaar Mohammad Mehdi | Issue Writer Certificate

Keywords

drought IndicesQ2

hazardsQ2

IsfahanQ2

machine learningQ2

remote sensingQ1

time series modelingQ1

Abstract

Drought has been known as a complex and perilous phenomenon in the whole world, particularly in Iran. Determining and predicting its severity can be effective in managing the hazards caused by drought. To determine drought severity, the indices have been used that can be divided into two broad categories of meteorological (M) and remotely-sensed (RS) indices. The most important M index has been the standardized perception index (SPI), and the most important RS indices have been those extracted from the vegetation index (NDVI) and land surface temperature (LST) index. For modeling time series behavior of these indices and also predicting their future values, machine learning methods proved to be highly efficient. This paper also aims to evaluate the performance of four important machine learning methods, i. e. neural network (NN), support vector regression (SVR), least squares support vector machine (LSSVM) and also an adaptive neuro fuzzy inference system (ANFIS) for modeling the M and RS indices of Eastern district of Isfahan during 2000 to 2014 and predicting their values in 2015 and 2016. The data used in this paper are the NDVI and LST time series of MODIS, and the rainfall time series of TRMM satellite of study area. At first, the vegetation condition index (VCI) and temperature vegetation index (TVX) have been built by NDVI and LST and 12-month SPI has been built by rainfall data. Next, the time series behavior of three these indices has been modeled by four aforementioned methods that according to the results, SVR has a highest efficiency and NN has a lowest efficiency among these methods. The performance speed of LSSVM and then ANFIS have been higher than the other methods. Finally by designing a fuzzy inference system (FIS), the drought severity at spring and summer of 2000 to 2016 has been monitored and the results have shown normality of spring in all years except 2000 and 2011 and severe drought in summer in all years except for the four years 2000, 2010, 2011 and 2014. In fact, this research has aimed to present a strategy for modeling drought behavior and predicting and monitoring it in future using machine learning methods and the remotely-sensed and meteorological time series data and fusing them in a FIS system.

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APA: Copy

KHOSRAVI, IMAN, AKHONDZADEH, MEHDI, & Khoshgoftaar, Mohammad Mehdi. (2015). Modeling and predicting the time series of drought indices ssing machine learning methods in order to manage hazards (Case study: Eastern district of Isfahan). ENVIRONMENTAL HAZARDS MANAGEMENT, 2(1 ), 51-65. SID. https://sid.ir/paper/265047/en

Vancouver: Copy

KHOSRAVI IMAN, AKHONDZADEH MEHDI, Khoshgoftaar Mohammad Mehdi. Modeling and predicting the time series of drought indices ssing machine learning methods in order to manage hazards (Case study: Eastern district of Isfahan). ENVIRONMENTAL HAZARDS MANAGEMENT[Internet]. 2015;2(1 ):51-65. Available from: https://sid.ir/paper/265047/en

IEEE: Copy

IMAN KHOSRAVI, MEHDI AKHONDZADEH, and Mohammad Mehdi Khoshgoftaar, “Modeling and predicting the time series of drought indices ssing machine learning methods in order to manage hazards (Case study: Eastern district of Isfahan),” ENVIRONMENTAL HAZARDS MANAGEMENT, vol. 2, no. 1 , pp. 51–65, 2015, [Online]. Available: https://sid.ir/paper/265047/en

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