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Determination and prediction of standardized precipitation index (SPI) using TRMM data in arid ecosystems

  • Amr Mossad
  • A. A. Alazba
Original Paper
  • 141 Downloads

Abstract

Drought over a period threatens the water resources, agriculture, and socioeconomic activities. Therefore, it is crucial for decision makers to have a realistic anticipation of drought events to mitigate its impacts. Hence, this research aims at using the standardized precipitation index (SPI) to predict drought through time series analysis techniques. These adopted techniques are autoregressive integrating moving average (ARIMA) and feed-forward backpropagation neural network (FBNN) with different activation functions (sigmoid, bipolar sigmoid, and hyperbolic tangent). After that, the adequacy of these two techniques in predicting the drought conditions has been examined under arid ecosystems. The monthly precipitation data used in calculating the SPI time series (SPI 3, 6, 12, and 24 timescales) have been obtained from the tropical rainfall measuring mission (TRMM). The prediction of SPI was carried out and compared over six lead times from 1 to 6 using the model performance statistics (coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE)). The overall results prove an excellent performance of both predicting models for anticipating the drought conditions concerning model accuracy measures. Despite this, the FBNN models remain somewhat better than ARIMA models with R ≥ 0.7865, MAE ≤ 1.0637, and RMSE ≤ 1.2466. Additionally, the FBNN based on hyperbolic tangent activation function demonstrated the best similarity between actual and predicted for SPI 24 by 98.44%. Eventually, all the activation function of FBNN models has good results respecting the SPI prediction with a small degree of variation among timescales. Therefore, any of these activation functions can be used equally even if the sigmoid and bipolar sigmoid functions are manifesting less adjusted R2 and higher errors (MAE and RMSE). In conclusion, the FBNN can be considered a promising technique for predicting the SPI as a drought monitoring index under arid ecosystems.

Keywords

Artificial intelligence SPI TRMM data Water resources 

Notes

Acknowledgements

Analyses and visualizations used in this paper were produced with the Giovanni online data system, developed and maintained by the NASA GES DISC.

Funding information

The Project was Financially Supported by King Saud University, Vice Deanship of Research Chairs.

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Copyright information

© Saudi Society for Geosciences 2018

Authors and Affiliations

  1. 1.Agricultural Engineering DepartmentKing Saud UniversityRiyadhKingdom of Saudi Arabia
  2. 2.Agricultural Engineering DepartmentAin Shams UniversityCairoEgypt
  3. 3.Alamoudi Water Research ChairKing Saud UniversityRiyadhKingdom of Saudi Arabia

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