Enhanced hybrid CEEMDAN-GMDH regression model for forewarning sucking pests in cotton crops of Coimbatore, Tamil Nadu
DOI:
https://doi.org/10.54386/jam.v27i4.3099Keywords:
Forewarning, Pest Management, Machine Learning, Population Dynamics, Sucking pests,, Decomposition techniquesAbstract
Effective pest management relies on early and accurate forecasting, yet current models struggle to capture regional specific complex relationship between weather conditions and pest incidence. This study addresses this gap by developing a robust crop pest forecasting model using the Group Method of Data Handling (GMDH) regression. We employed three decomposition techniques like Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to break down nonlinear data into Intrinsic Mode Functions (IMFs). These IMFs were then predicted using GMDH regression, incorporating weather variables as independent factors. The ensemble forecasts were constructed by aggregating the predicted IMFs. The study utilized pest incidence data from 2015 to 2023 for aphid, jassid, thrips, and whitefly pests. Findings indicated that the CEEMDAN-GMDH model outperformed others for forecasting the incidence of aphid, thrips, and whitefly pests, with improvements of 16.3%, 4.3%, and 13.6% over the univariate GMDH model, respectively. For jassid, the EEMD-GMDH model provided the best forecasts, despite CEEMDAN’s superior decomposition capabilities. The study concludes that integrating decomposition methods, with GMDH regression provides a more reliable tool for predicting pest incidences in cotton crops, thereby aiding in better pest management strategies.
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