A Next-Day Dew Intensity Prediction Model Based on the Improved Hippopotamus Optimization

Accurate dew intensity prediction is vital in multiple fields, such as agriculture, meteorology, industry, and transportation. This study addresses the cross-disciplinary demands for dew intensity prediction by proposing a hybrid deep learning model based on the improved hippopotamus optimization (IHO). Key influencing factors were selected through multidimensional meteorological data correlation analysis, and a fusion architecture of a Bidirectional Temporal Convolutional Network (BiTCN) and a Support Vector Machine (SVM) was constructed. The IHO algorithm is adopted to optimize model parameters and enhance prediction accuracy adaptively. Experiments were conducted using ten years of meteorological data to verify the prediction of twelve-hour dew intensity in three typical ecosystems in Northeast China: farmland, marsh wetland, and urban areas. The results show that the optimized IHO-BiTCN-SVM model achieved significant improvements in key indicators, including MAE, MAPE, MSE, RMSE, and R². For the farmland ecosystem, MAE was reduced by 72.2% (0.0016572 vs. 0.0059659), MSE decreased from 6.8552 × 10⁻⁵ to 6.7874 × 10⁻⁶, and R² increased by 12.5% (0.98791 vs. 0.87793). The IHO algorithm reduced the MAE of the farmland system by 39.6%, the MAPE by 41.6%, and the MSE by 60.2%, yet the R² increased by 1.8% compared with the benchmark model. This model effectively overcomes the subjectivity of traditional methods through an intelligent parameter optimization mechanism, providing reliable technical support for precise agricultural irrigation decisions, urban dew formation warnings, and wetland ecological protection.