基于改进型 GRU 循环神经网络的土壤水分预测模型

Guowei Wang, Chunying Wei, Li Yan, Jian Li
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引用次数: 0

摘要

土壤水分在土地水能循环中起着至关重要的作用,对天气和气候变化也有一定的影响。在农业生产中,可以根据土壤墒情判断作物墒情状况,制定及时有效的灌溉策略,在保证粮食产量的同时节约水资源,实现农业水资源利用价值的最大化和可持续发展。因此,土壤水分预测的准确性对农业、气候等诸多领域具有重要的研究价值。本文利用少量影响因素,即大气温度、大气湿度、降雨量和土壤水分数据,通过智能海鸥优化算法对 GRU 循环神经网络的超级参数进行优化,建立了土壤水分预测模型。该模型分别用于预测未来 12 小时、24 小时、36 小时和 48 小时的土壤水分。最终实验结果表明,本文模型对土壤墒情的预测效果较好,最佳预测评价指标数据为MAPE(12h)=4.4120%,R2(12h)=0.94605,RMSE(12h)=1.9998;通过纵向比较多个时间步长的预测结果,发现本文模型的预测精度下降较为平稳,满足土壤墒情预测的要求。
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Soil Moisture Prediction Model Based on Improved GRU Recurrent Neural Network
Soil moisture plays a crucial role in land water and energy cycles, and has a certain impact on weather and climate change. In agricultural production, crop moisture status can be determined based on soil moisture, and timely and effective irrigation strategies can be formulated to ensure grain yield while saving water resources, maximizing the value of agricultural water resource utilization, and achieving sustainable development. Therefore, the accuracy of soil moisture prediction has important research value for many fields such as agriculture and climate. In this paper, the super parameters of GRU Recurrent neural network are optimized by intelligent seagull optimization algorithm using a small number of influencing factors, namely, atmospheric temperature, atmospheric humidity, rainfall and soil moisture data, and a soil moisture prediction model is established. The model was used to predict soil moisture for the next 12 hours, 24 hours, 36 hours, and 48 hours, respectively. The final experiment showed that the model in this paper had better predictive effect on soil moisture, with the best predictive evaluation index data being MAPE (12h) = 4.4120%, R2 (12h) = 0.94605, and RMSE (12h) = 1.9998; By comparing the prediction results of multiple time steps vertically, it was found that the prediction accuracy of the model in this paper decreased more smoothly, meeting the requirements of soil moisture prediction.
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来源期刊
Strategic Planning for Energy and the Environment
Strategic Planning for Energy and the Environment Environmental Science-Environmental Science (all)
CiteScore
1.50
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0.00%
发文量
25
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