Enhancing Drought Forecast Accuracy Through Informer Model Optimization
As droughts become more frequent due to climate change and shifts in land use, enhancing the accuracy of drought prediction is becoming crucial for informed land and water resource management. This study employed the Informer model to forecast drought and conducted a comparative analysis with Autore...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/14/1/126 |
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| author | Jieru Wei Wensheng Tang Pakorn Ditthakit Jiandong Shang Hengliang Guo Bei Zhao Gang Wu Yang Guo |
| author_facet | Jieru Wei Wensheng Tang Pakorn Ditthakit Jiandong Shang Hengliang Guo Bei Zhao Gang Wu Yang Guo |
| author_sort | Jieru Wei |
| collection | DOAJ |
| description | As droughts become more frequent due to climate change and shifts in land use, enhancing the accuracy of drought prediction is becoming crucial for informed land and water resource management. This study employed the Informer model to forecast drought and conducted a comparative analysis with Autoregressive Integrated Moving Average (ARIMA), long short-term memory (LSTM), and Convolutional Neural Network (CNN) models. The findings indicate that the Informer model outperforms the other three models in terms of drought forecasting accuracy across all time scales. Nevertheless, the predictive capacity of the Informer model remains suboptimal when it comes to short-term intervals. Aiming at the problem of drought forecasting accuracy in a short time scale, this study proposed a drought forecasting model named VMD-JAYA-Informer based on Variational Mode Decomposition (VMD) and the JAVA optimization algorithm to improve the Informer model. This study conducted a comparative analysis of VMD-JAYA-ARIMA, VMD-JAYA-LSTM, VMD-JAYA-CNN, and VMD-JAYA-Informer drought prediction models. The performance of these models was evaluated using the root mean square error (RMSE), Nash–Sutcliffe efficiency coefficient (NSE), and Mean Absolute Error (MAE). The VMD-JAYA-Informer model’s forecast for the 1-month SPEI significantly surpasses that of alternative models and demonstrates a robust agreement with the actual data. Simultaneously, the model exhibits equally optimal forecasting performance across different time scales. In order to validate the VMD-JAYA-Informer model, four meteorological stations in the Songliao River Basin were chosen at random. The validation results demonstrate that VMD-JAYA-Informer outperforms the Informer model in terms of prediction accuracy on the 1-month time scale (NSE values of 0.8663, 0.8765, 0.8822, and 0.8416, respectively). Additionally, the model outperforms Informer in terms of prediction performance on other time scales, further demonstrating its generalizability and excellence in drought prediction on shorter time scales. |
| format | Article |
| id | doaj-art-66864739e31447708b7db70e16270853 |
| institution | Kabale University |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-66864739e31447708b7db70e162708532025-01-24T13:38:00ZengMDPI AGLand2073-445X2025-01-0114112610.3390/land14010126Enhancing Drought Forecast Accuracy Through Informer Model OptimizationJieru Wei0Wensheng Tang1Pakorn Ditthakit2Jiandong Shang3Hengliang Guo4Bei Zhao5Gang Wu6Yang Guo7The School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, ChinaThe School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, ChinaCenter of Excellence in Sustainable Disaster Management, School of Engineering and Technology, Walailak University, Nakhon Si Thammarat 80161, ThailandThe School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, ChinaThe School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, ChinaThe School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, ChinaThe School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, ChinaThe School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, ChinaAs droughts become more frequent due to climate change and shifts in land use, enhancing the accuracy of drought prediction is becoming crucial for informed land and water resource management. This study employed the Informer model to forecast drought and conducted a comparative analysis with Autoregressive Integrated Moving Average (ARIMA), long short-term memory (LSTM), and Convolutional Neural Network (CNN) models. The findings indicate that the Informer model outperforms the other three models in terms of drought forecasting accuracy across all time scales. Nevertheless, the predictive capacity of the Informer model remains suboptimal when it comes to short-term intervals. Aiming at the problem of drought forecasting accuracy in a short time scale, this study proposed a drought forecasting model named VMD-JAYA-Informer based on Variational Mode Decomposition (VMD) and the JAVA optimization algorithm to improve the Informer model. This study conducted a comparative analysis of VMD-JAYA-ARIMA, VMD-JAYA-LSTM, VMD-JAYA-CNN, and VMD-JAYA-Informer drought prediction models. The performance of these models was evaluated using the root mean square error (RMSE), Nash–Sutcliffe efficiency coefficient (NSE), and Mean Absolute Error (MAE). The VMD-JAYA-Informer model’s forecast for the 1-month SPEI significantly surpasses that of alternative models and demonstrates a robust agreement with the actual data. Simultaneously, the model exhibits equally optimal forecasting performance across different time scales. In order to validate the VMD-JAYA-Informer model, four meteorological stations in the Songliao River Basin were chosen at random. The validation results demonstrate that VMD-JAYA-Informer outperforms the Informer model in terms of prediction accuracy on the 1-month time scale (NSE values of 0.8663, 0.8765, 0.8822, and 0.8416, respectively). Additionally, the model outperforms Informer in terms of prediction performance on other time scales, further demonstrating its generalizability and excellence in drought prediction on shorter time scales.https://www.mdpi.com/2073-445X/14/1/126drought forecastingmulti-time scalesInformerVMDJAYAVMD-JAYA-Informer |
| spellingShingle | Jieru Wei Wensheng Tang Pakorn Ditthakit Jiandong Shang Hengliang Guo Bei Zhao Gang Wu Yang Guo Enhancing Drought Forecast Accuracy Through Informer Model Optimization Land drought forecasting multi-time scales Informer VMD JAYA VMD-JAYA-Informer |
| title | Enhancing Drought Forecast Accuracy Through Informer Model Optimization |
| title_full | Enhancing Drought Forecast Accuracy Through Informer Model Optimization |
| title_fullStr | Enhancing Drought Forecast Accuracy Through Informer Model Optimization |
| title_full_unstemmed | Enhancing Drought Forecast Accuracy Through Informer Model Optimization |
| title_short | Enhancing Drought Forecast Accuracy Through Informer Model Optimization |
| title_sort | enhancing drought forecast accuracy through informer model optimization |
| topic | drought forecasting multi-time scales Informer VMD JAYA VMD-JAYA-Informer |
| url | https://www.mdpi.com/2073-445X/14/1/126 |
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