Advanced stacked integration method for forecasting long-term drought severity: CNN with machine learning models

Abstract

Study region: Eight governorates in upper Egypt namely Aswan, Asyut, Beni-Suef, Fayoum, Luxor, Minya, Qena and Sohag. Study focus: This study aims to develop novel hybrid machine learning (ML) models for forecasting the drought phenomena based on limited inputs for the eight Egyptian govern-orates, and ii) evaluate the performance and accuracy of the developed ML models for predicting Palmer Drought Severity Index (PDSI) to recommend the optimal model based on performance statistical metrics. The hybrid ML models were Convolution Neural Networks (CNN)-Long Short-Term Memory (LSTM), CNN-Random Forest (RF), CNN-Support Vector Machine (SVR), and CNN-Extreme Gradient Boosting (XGB). New hydrological insights for the region: Results showed that CNN-LSTM model outperformed the others followed by CNN-RF. Values of NSE, MAE, MARE, IA, R2, and RMSE for CNN-LSTM were 0.885, 0.915, − 2.073, 0.967, 0.885, and 0.573, respectively. For the testing stage CNN-SVR model was found to perform the best; average values of NSE, MAE, MARE, IA, R2, and RMSE were 0.828, 0.364, − 2.903, 0.950, 0.828 and 0.688, respectively. This study provided a way forward for convenient estimation of the PDSI Index from the meteorological data in terms of advancing deep learning algorithms. The developed hybrid models, more or less, can satisfactory predict PDSI values. Additionally, the study suggests the CNN-LSTM model as the most suitable model to advance future investigation in the study area.

Keywords

• PDSI models
• Long short-term memory
• Convolution Neural Networks
• Extreme gradient boosting
• Support vector machine
• Upper Egypt