Application of Extreme Learning Machine Algorithm for Drought Forecasting
Drought is a complex and frequently occurring natural hazard in many parts of the world. Therefore, accurate drought forecasting is essential to mitigate its adverse impacts. This research has inferred the implication and the appropriateness of the extreme learning machine (ELM) algorithm for drough...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/4998200 |
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| author | Muhammad Ahmad Raza Mohammed M. A. Almazah Zulfiqar Ali Ijaz Hussain Fuad S. Al-Duais |
| author_facet | Muhammad Ahmad Raza Mohammed M. A. Almazah Zulfiqar Ali Ijaz Hussain Fuad S. Al-Duais |
| author_sort | Muhammad Ahmad Raza |
| collection | DOAJ |
| description | Drought is a complex and frequently occurring natural hazard in many parts of the world. Therefore, accurate drought forecasting is essential to mitigate its adverse impacts. This research has inferred the implication and the appropriateness of the extreme learning machine (ELM) algorithm for drought forecasting. For numerical evaluation, time series data of the Standardized Precipitating Temperature Index (SPTI) are used for nine meteorological stations located in various climatological zones of Pakistan. To assess the performance of ELM, this research includes parallel inferences of multilayer perceptron (MLP) and autoregressive integrated moving average (ARIMA) models. The performance of each model is assessed using root mean square error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), Kling-Gupta efficiency (KGE), Willmott index (WI), and Karl Pearson’s correlation coefficient. Generally, graphical results illustrated an excellent performance of the ELM algorithm over MLP and ARIMA models. For training data of SPTI-1, ELM’s best performance has observed at Chitral station (RMSE = 0.374, KGE = 0.838, WI = 0.960, MAE = 0.272, MAPE = 259.59, R = 0.93). For SPTI-1 at Astore station, the numerical results are (RMSE = 0.688, KGE = 0.988, WI = 0.997, MAE = 0.798, MAPE = 247.35). The overall results indicate that the ELM outperformed by producing the smallest RMSE, MAE, and MAPE values and maximum values for KGE, WI, and correlation coefficient values at almost all the selected meteorological stations for (1, 3, 6, 9, and 12) month time scales. In summary, this research endorses the use of ELM for accurate drought forecasting. |
| format | Article |
| id | doaj-art-7b47e47737dc473785ca920743f327c0 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-7b47e47737dc473785ca920743f327c02025-02-03T01:23:35ZengWileyComplexity1099-05262022-01-01202210.1155/2022/4998200Application of Extreme Learning Machine Algorithm for Drought ForecastingMuhammad Ahmad Raza0Mohammed M. A. Almazah1Zulfiqar Ali2Ijaz Hussain3Fuad S. Al-Duais4Department of StatisticsDepartment of MathematicsCollege of Statistical and Actuarial SciencesDepartment of StatisticsMathematics DepartmentDrought is a complex and frequently occurring natural hazard in many parts of the world. Therefore, accurate drought forecasting is essential to mitigate its adverse impacts. This research has inferred the implication and the appropriateness of the extreme learning machine (ELM) algorithm for drought forecasting. For numerical evaluation, time series data of the Standardized Precipitating Temperature Index (SPTI) are used for nine meteorological stations located in various climatological zones of Pakistan. To assess the performance of ELM, this research includes parallel inferences of multilayer perceptron (MLP) and autoregressive integrated moving average (ARIMA) models. The performance of each model is assessed using root mean square error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), Kling-Gupta efficiency (KGE), Willmott index (WI), and Karl Pearson’s correlation coefficient. Generally, graphical results illustrated an excellent performance of the ELM algorithm over MLP and ARIMA models. For training data of SPTI-1, ELM’s best performance has observed at Chitral station (RMSE = 0.374, KGE = 0.838, WI = 0.960, MAE = 0.272, MAPE = 259.59, R = 0.93). For SPTI-1 at Astore station, the numerical results are (RMSE = 0.688, KGE = 0.988, WI = 0.997, MAE = 0.798, MAPE = 247.35). The overall results indicate that the ELM outperformed by producing the smallest RMSE, MAE, and MAPE values and maximum values for KGE, WI, and correlation coefficient values at almost all the selected meteorological stations for (1, 3, 6, 9, and 12) month time scales. In summary, this research endorses the use of ELM for accurate drought forecasting.http://dx.doi.org/10.1155/2022/4998200 |
| spellingShingle | Muhammad Ahmad Raza Mohammed M. A. Almazah Zulfiqar Ali Ijaz Hussain Fuad S. Al-Duais Application of Extreme Learning Machine Algorithm for Drought Forecasting Complexity |
| title | Application of Extreme Learning Machine Algorithm for Drought Forecasting |
| title_full | Application of Extreme Learning Machine Algorithm for Drought Forecasting |
| title_fullStr | Application of Extreme Learning Machine Algorithm for Drought Forecasting |
| title_full_unstemmed | Application of Extreme Learning Machine Algorithm for Drought Forecasting |
| title_short | Application of Extreme Learning Machine Algorithm for Drought Forecasting |
| title_sort | application of extreme learning machine algorithm for drought forecasting |
| url | http://dx.doi.org/10.1155/2022/4998200 |
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