Drought Forecasting using Wavelet-GMDH Model with Standardized Precipitation Index
Mohammed Salisu Alfa1, Ani Bin Shabri2, Muhammad Akram Shaari3
1Mohammed Salisu Alfa , Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia (UTM) 81300 Johor, Malaysia
Ani Bin Shabri, Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia (UTM) 81300 Johor, Malaysia
3Muhammad Akram Shaari, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM) 81300 Johor, Malaysia.

Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 1431-1435 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7402118419/2019©BEIESP | DOI: 10.35940/ijrte.D7402.118419

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This paper proposes Wavelet-Group Methods of Data Handling (W-GMDH) model to explore its ability of drought forecasting. The W-GMDH model was developed by combining Discrete Wavelet Transform (DWT) and GMDH model using the Standardized Precipitation Index (SPI) drought data for forecasting to assess the effectiveness of the new (W-GMDH) model. These methods were used on four SPI data sets (SPI3, SPI6, SPI9 and SPI12). To achieve this, a 624 month of SPI data from January 1956 to December 2008 was used and divided into two parts (80% for training and 20% for testing). The results of the W-GMDH model were then compared with the conventional GMDH model using Root Mean Square Error (RMSE), Mean Average Error (MAE) and coefficient of correlation as the performance evaluation measures. Both results of the proposed W-GMDH model and the GMDH showed very clearly that the propose method can achieve the best forecasting performance in terms of accuracy for each of the SPI data series. The key role played by the DWT is to smooth the analysis of SPI data obtained after the wavelet decomposition which is also used to decompose the SPI data into different number of component series to minimize the forecasting error. In all the results computed, the proposed model has a minimum error indicating its superiority over the GMDH model. This indicates that W-GMDH model’s performance has outweighed that of the conventional GMDH model in SPI drought forecasting. The research contributes to the discovering of viable forecasting of drought and demonstrates that the established model is good and appropriate for drought. In all the analysis W-GMDH model has the minimum error. The overall results showed that SPI12 has the minimum error among all the SPI data considered.
Keywords: Drought Forecasting; GMDH; Time Series; SPI; DWT.
Scope of the Article: Behaviour of Structures.