热带气象学报2024，Vol.40Issue(6)：1030-1044,15.DOI:10.16032/j.issn.1004-4965.2024.091
基于神经网络模型可解释性的降水预报
Precipitation Forecasting Based on the Interpretability of Neural Network Models
摘要
Abstract
To improve the accuracy and reliability of localized,fine-scale precipitation forecasts,the present study proposed a new neural network capable of precipitation forecasting based on KernelExplainer and clustering of interpretable Shapley additive explanations(SHAP)values.First,the output jitter in the neural network was addressed by using normalized distribution transformation.Subsequently,we estimated the deep learning neural network model comprising convolutional(CNN)layers,long short-term memory(LSTM)networks,and dense layers using the KernelExplainer.This process yielded SHAP values that represent the contributions of meteorological parameter m and time step parameter tl to forecasting results.Finally,by dynamically adjusting the model's m and tl parameters through SHAP value clustering in each rolling forecast,we managed to use the method to improve forecasting performance for non-precipitation and heavy precipitation events.Using this method,a precipitation forecasting model for the Atmospheric Observation Station of Nanjing University of Information Science&Technology was established based on observational data and numerical weather prediction model outputs from January 2018 to December 2023.Experimental results show that,compared to fixed-parameter models,multilayer ConvLSTM models,Analog-Ensemble-CNN models,and numerical weather prediction models,the proposed model reduced the mean absolute error of precipitation forecasts by 8%,7%,11%,and 19%,respectively.关键词
卷积/长短期记忆/ReLU激活函数/KernelExplainer/SHAP/降水预报Key words
convolution/long short-term memory/ReLU activation function/KernelExplainer/Shapley additive explanations(SHAP)/precipitation forecasting分类
天文与地球科学引用本文复制引用
樊仲欣,王妍,王若曈..基于神经网络模型可解释性的降水预报[J].热带气象学报,2024,40(6):1030-1044,15.基金项目
国家自然科学基金项目(42075115) （42075115）
江苏省自然科学基金项目(BK20221344)共同资助 （BK20221344）
