Version 1
: Received: 22 July 2024 / Approved: 23 July 2024 / Online: 24 July 2024 (07:14:47 CEST)
How to cite:
Sibiya, S.; Mbatha, N.; Ramroop, S.; Melesse, S.; Silwimba, F. Forecasting of Standardized Precipitation Index using Hybrid Models: A Case Study of Cape Town, South Africa. Preprints2024, 2024071807. https://doi.org/10.20944/preprints202407.1807.v1
Sibiya, S.; Mbatha, N.; Ramroop, S.; Melesse, S.; Silwimba, F. Forecasting of Standardized Precipitation Index using Hybrid Models: A Case Study of Cape Town, South Africa. Preprints 2024, 2024071807. https://doi.org/10.20944/preprints202407.1807.v1
Sibiya, S.; Mbatha, N.; Ramroop, S.; Melesse, S.; Silwimba, F. Forecasting of Standardized Precipitation Index using Hybrid Models: A Case Study of Cape Town, South Africa. Preprints2024, 2024071807. https://doi.org/10.20944/preprints202407.1807.v1
APA Style
Sibiya, S., Mbatha, N., Ramroop, S., Melesse, S., & Silwimba, F. (2024). Forecasting of Standardized Precipitation Index using Hybrid Models: A Case Study of Cape Town, South Africa. Preprints. https://doi.org/10.20944/preprints202407.1807.v1
Chicago/Turabian Style
Sibiya, S., Sileshi Melesse and Felix Silwimba. 2024 "Forecasting of Standardized Precipitation Index using Hybrid Models: A Case Study of Cape Town, South Africa" Preprints. https://doi.org/10.20944/preprints202407.1807.v1
Abstract
Droughts have negative impacts on agricultural productivity and economic growth. Effective monitoring and accurate forecasting of drought occurrences and trends are crucial for minimizing drought losses and mitigating their spatial and temporal effects. In this study, trend dynamics in monthly total rainfall time series measured at Cape Town International Airport were analyzed using Mann-Kendall (MK) test, Modified Mann-Kendall (MMK) and innovative trend analysis (ITA) were examined. Additionally, we utilized hybrid prediction method that combined the model with the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique, the autoregressive integrated moving average (ARIMA) model, the long short-term memory (LSTM) network (i.e. CEEMDAN-ARIMA-LSTM) to forecast SPI values of 6-, 9-, and 12-months using rainfall data between 1995 and 2020 from Cape Town International Airport meteorological rainfall stations. The results demonstrate the following essential points: (1) The trend analysis results of the total monthly rainfall values indicate a significant decreasing trend with the negative z-score (MK= -3.7541 and MMK= - 4.0773). ITA also indicated a significant downwards trend of rainfall in the study area. (2) As the forecasting horizon increases, the prediction accuracy of the models and CEEMDAN-combined models gradually improves, reaching their optimum performance at the 12-month horizon. (3) CEEMDAN efficiently stabilizes time-series data, resulting in greater prediction accuracy in the hybrid model compared to individual models at all timescales. (4) The RMSE and R2 values for the hybrid CEEMDAN-ARIMA-LSTM model at a 12-month forecasting horizon are 0.042 and 0.995, demonstrating the applicability of this hybrid approach for drought forecasting.
Keywords
Drought forecasting; Standardized Precipitation Index; CEEMDAN; hybrid model
Subject
Environmental and Earth Sciences, Water Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
