مشخصات پژوهش

صفحه نخست /A Novel Feature ...
عنوان A Novel Feature Extraction-Selection Technique for Long Lead Time Agricultural Drought Forecasting
نوع پژوهش مقاله چاپ‌شده
کلیدواژه‌ها Drought Forecasting, Feature Extraction, Feature Selection, Gaussian Process Regression, Long Lead Time Forecasting, Long Short-Term Memory.
چکیده Long-term drought forecasting plays a crucial role in mitigating drought risks by providing early warnings. Researchers have long been interested in achieving accurate long-term drought forecasting, which is challenging since accuracy generally decreases by increasing the forecasting period. The primary aim of this research is to propose a new method for high-accuracy long lead time drought forecasting by combining various Feature Extraction (FE) and selection techniques. In this study, monthly time-series datasets encompassing precipitation, potential evapotranspiration, actual evapotranspiration, runoff, surface and root-zone soil moisture—were utilized to forecast SPEI-6 over various lead times including 1-, 3-, 6-, 9-, 12-, 18-, and 24-months using global gridded products with a 0.5O ×0.5O spatial resolution spanning the years January 1980 to December 2022. The method was evaluated using two different approaches, namely Gaussian Process Regression (GPR) as a simple machine learning technique and Long Short-Term Memory (LSTM) as a deep learning method. The findings provided improved accuracy, particularly for long-term forecasting when employing the proposed methodology. When utilizing LSTM with FE instead of the original datasets as inputs, the error reduced from RMSE=0.16 to RMSE=0.07 (a 56% decrease), while the correlation increased from R=0.65 to R=0.90 (a 38% increase) when forecasting SPEI-6 12 months ahead. The results showed that the GPR with FE and selection model outperformed the LSTM with original datasets model for SPEI-6 (t+24) with a correlation coefficient (R) of 0.9811 and a Normalized Root Mean Square Error (NRMSE) of 0.1380, compared to R=0.6517 and NRMSE=0.4307 for the LSTM with original datasets. These findings can offer valuable insights for early agricultural drought warning in arid areas.
پژوهشگران مه نوش مقدسی (نفر سوم)، مهدی محمدی قلعه نی (نفر اول)، منصور مرادی (نفر دوم)