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基于PSWE模型的土壤水鹽運移與夏玉米生產效益模擬
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國家自然科學基金項目(51669019)和國家自然科學基金重點項目(51539005)


Simulation of Soil Salt-water Migration and Summer Maize Productivity Based on PSWE Model
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    摘要:

    為實現多因素影響下土壤水鹽,、作物生產效益間的雙層遞進因果關系模擬,基于深度學習理論及方法將分級長短期記憶網絡(HLSTM)與批標準化多層感知機(BMLP)耦合,,且將Dropout與Adam優(yōu)化算法耦合作為面向收斂的改進算法,,構建了遞進水鹽嵌入神經網絡(Progressive salt-water embedding neural network,PSWE)模型,。評估了PSWE模型的有效性,,并開展了多因素協同秸稈深埋下不同灌水量的土壤水鹽及夏玉米生產效益的模擬。結果表明,,PSWE模型具有多因素整體協同性,,有效地模擬了土壤水鹽運移規(guī)律、夏玉米生產效益及各變量間的內在依存關系,。模型平均均方根誤差為0.031,,平均絕對誤差為0.569,平均決定系數為0.987,。模擬結果表明,,單次灌水60mm的耕作層(0~40cm)含水率隨時間推移持續(xù)降低,,單次灌水135mm的耕作層含水率變幅較大,成熟期二者在秸稈隔層積鹽率分別為49.2%和11.2%,;單次灌水90mm和120mm的耕作層含水率保持在16%~24%之間,,成熟期二者在大于40cm土層含水率保持平穩(wěn),秸稈隔層有脫鹽趨勢,,脫鹽率為6.1%和5.9%,;夏玉米單次理論灌水量為89.3~96.8mm,耕作層理論含鹽量為1.38~1.55g/kg,。綜上,,多因素協同秸稈深埋下適宜灌溉量可實現抑鹽提效的目標,PSWE模型可有效模擬土壤水鹽運移和作物生產效益,,為深度學習理論及技術在土壤水鹽運移模型上的應用提供參考,。

    Abstract:

    To realize simulation of the two-layer progressive causal relationship of soil salt-water and crop production benefits under the influence of multiple factors, based on deep learning theory and technology, the progressive salt-water embedding neural network (PSWE) model was constructed. In PSWE model, the time serialized data encoder framed by hierarchical long short-term memory (HLSTM) and decoder framed batch-normalized multi-layer perceptron (BMLP) were coupled, and the coupling between Dropout and Adam algorithm was optimized as an improved algorithm for convergence regression. The validity of PSWE model was evaluated, and the dynamic changes of soil water-salt of different irrigation amounts under multi-factors cooperative straw deep burial were simulated, and the production benefit of summer maize was predicted. The results showed that PSWE model had multivariable overall synergy, self-learning habit and high accuracy. PSWE model could effectively describe the law of soil salt-water migration under straw deep burial in Hetao Irrigation District, the internal dependence relationship between summer maize production benefit and various variables. The root mean square error of the PSWE model was 0.031, the mean absolute error was 0.569, and the determination coefficient was 0.987. Through the model simulation, along with the summer maize growth period, the moisture content of treatment of single irrigation 60mm was reduced continuously in the tillage layer (0~40cm), and affected the summer maize for normal growth, while the change of treatment of 135mm was larger. In the mature stage, they produced salt accumulation in the straw inter-layer, and the salt accumulation rate was 49.2% and 11.2%. The water content in the tillage layer of single irrigation 90mm and 120mm were kept between 16% and 24%. At the end of the growth period, the water content in the soil layer over 40cm was kept stable. The straw inter-layer showed a trend of desalting, and the desalting rate was 6.1% and 5.9%, respectively. It was suggested that the single irrigation amount should be 89.3~96.8mm,and the theoretical salt content of cultivated layer was 1.38~1.55g/kg. In conclusion, under multi-factors cooperative straw deep burial, appropriate irrigation amount could achieve the goal of salt suppression effect and improvement of water use efficiency. The PSWE model could effectively simulate soil salt-water migration. The simulation of soil water-salt migration and crop productivity benefit by PSWE model was applicable, which provided a reference for deep learning theory and technology in soil salt-water migration.

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張萬鋒,楊樹青,胡睿琦,鄂繼芳.基于PSWE模型的土壤水鹽運移與夏玉米生產效益模擬[J].農業(yè)機械學報,2022,53(6):359-369. ZHANG Wanfeng, YANG Shuqing, HU Ruiqi, E Jifang. Simulation of Soil Salt-water Migration and Summer Maize Productivity Based on PSWE Model[J]. Transactions of the Chinese Society for Agricultural Machinery,2022,53(6):359-369.

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  • 收稿日期:2021-07-07
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  • 在線發(fā)布日期: 2021-07-30
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