中国生态农业学报（中英文）2026，Vol.34Issue(1)：45-57,13.DOI:10.12357/cjea.20250282
基于改进鲸鱼优化算法的种植业碳排放预测
Prediction of carbon emissions from the planting industry based on the improved whale optimization algorithm
摘要
Abstract
Carbon emissions from the planting industry are a significant source of greenhouse gas emissions.The accurate prediction and effective management of these emissions are crucial for mitigating climate change and promoting sustainable agricultural devel-opment.Conventional prediction models exhibit a limited capability to capture the complex nonlinear interactions inherent in carbon emission systems of the planting industry,and their insufficient robustness often leads to overfitting.In this study,we used the plant-ing industry in Heilongjiang Province as a case study to explore how to optimize existing methods for predicting carbon emissions from the planting industry.First,the IPCC carbon emission method is applied to comprehensively account for three major sources of carbon emissions:carbon emissions from agricultural land use,CH4 emissions from rice field,and N2O emissions from agricultural land.Carbon emissions from planting activities in Heilongjiang Province from 2001 to 2022 were systematically calculated.Based on this,a long short-term memory(LSTM)network model was developed,incorporating three key dimensions:social and economic drivers,production scale effects,and technical energy consumption intensity.To enhance the predictive performance of the model,an improved whale optimization algorithm(IWOA)was introduced to optimize four hyperparameters of the LSTM model:number of hidden units,learning rate,batch size,and training epochs.Then,the IWOA-LSTM model was used to predict future carbon emis-sions from the planting industry in Heilongjiang Province from 2023 to 2027 under both baseline and low-carbon scenarios.The res-ults were showed as below.1)Carbon emissions from the planting industry in Heilongjiang Province showed a trend of"rapid growth followed by a fluctuating decline",reaching a peak of 20.45 million t in 2015.The main sources of carbon emissions included CH4 emissions from rice field,N2O emissions from agricultural land,and carbon emissions resulting from fertilizer production and applic-ation;their average proportions in the total annual emissions were 41.42％,38.26％,and 11.65％,respectively.2)Compared with the unoptimized LSTM model,the IWOA-LSTM model demonstrated significant improvements in both the prediction accuracy and sta-bility.It achieved a mean absolute error of 55.82×104t,root mean square error of 61.74×104t,and mean absolute percentage error of 2.83％,all of which were superior to those of the LSTM model(114.41 ×104 t,124.72×104t,and 5.78％).In this study,we demon-strated that the IWOA-LSTM model could effectively predict carbon emissions from the planting industry,thereby providing a sci-entific basis for the formulation of carbon reduction policies for the planting industry in Heilongjiang Province.3)The prediction res-ults of the IWOA-LSTM model showed that carbon emissions from the planting industry in Heilongjiang Province could be effect-ively suppressed by controlling the crop planting area,improving fertilizer application efficiency,and reducing diesel consumption per unit area of agricultural machinery.Based on the aforementioned conclusions,the following recommendations for emission reduc-tion are proposed:optimizing land-use structure and controlling crop planting area,increasing the application and innovation of green agricultural technologies,promoting rural economic development,increasing farmers' income,and strengthening policy support and incentive mechanisms.Through the above measures,the sustainable development of agriculture in Heilongjiang Province can be fur-ther achieved.关键词
种植业碳排放/长短期记忆网络/鲸鱼优化算法/时间切分交叉验证Key words
carbon emissions from the planting industry/long short-term memory network/whale optimization algorithm/time-split cross-validation分类
资源环境引用本文复制引用
郭静,尚杰..基于改进鲸鱼优化算法的种植业碳排放预测[J].中国生态农业学报（中英文）,2026,34(1):45-57,13.基金项目
黑龙江省哲学社会科学研究规划年度项目(24GLC023)和国家社会科学基金后期资助项目(20FGLB059)资助 This study was supported by the Philosophy and Social Science Research Planning Annual Project of Heilongjiang Province(24GLC023),and the Post-funded Project of the National Social Science Foundation of China(20FGLB059). （24GLC023）
