Drought Forecasting using Markov Chain Model and Artificial Neural Networks

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• 作者：Mehdi Rezaeianzadeh ; Alfred Stein ; Jonathan Peter Cox
• 关键词：Reservoir inflow ; Markov chain ; Data ; driven models ; Drought forecasting ; Reservoir operation ; ANN
• 刊名：Water Resources Management
• 出版年：2016
• 出版时间：May 2016
• 年：2016
• 卷：30
• 期：7
• 页码：2245-2259
• 全文大小：771 KB
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• 作者单位：Mehdi Rezaeianzadeh (1)
  Alfred Stein (2)
  Jonathan Peter Cox (3)
  
  1. School of Forestry and Wildlife Sciences, Auburn University, 602 Duncan Drive, Auburn, AL, 36849, USA
  2. Faculty of Geo-Information Science and Earth Observation (ITC), Twente University, Enschede, The Netherlands
  3. Caribbean Institute for Meteorology and Hydrology, West Indies, Barbados
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Hydrogeology
  Geotechnical Engineering
  Meteorology and Climatology
  Civil Engineering
  Environment
  
• 出版者：Springer Netherlands
• ISSN：1573-1650

文摘

Water resources management is a complex task. It requires accurate prediction of inflow to reservoirs for the optimal management of surface resources, especially in arid and semi-arid regions. It is in particular complicated by droughts. Markov chain models have provided valuable information on drought or moisture conditions. A complementary method, however, is required that can both evaluate the accuracy of the Markov chain models for predicted drought conditions, and forecast the values for ensuing months. To that end, this study draws on Artificial Neural Networks (ANNs) as a data-driven model. The employed ANNs were trained and tested by means of a statistically-based input selection procedure to accurately predict reservoir inflow and consequently drought conditions. Thirty three years’ data of inflow volume on a monthly time resolution were selected to enable calculation of the standardized streamflow index (SSI) for the Markov chain model. Availability of hydro-climatic data from the Doroodzan reservoir in the Fars province, Iran, allowed us to develop a reservoir specific ANN model. Results demonstrated that both models accurately predicted drought conditions, by employing a randomization procedure that facilitated the selection of the required data for the ANN to forecast reservoir inflow close to the observed values over a validation period. The results confirmed that combining the two models improved short-term prediction reliability. This was in contrast to single model applications that resulted into substantial uncertainty. This research emphasized the importance of the correct selection of data or data mining, prior to entering a specific modeling routine.