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科技导报
| 研究论文 2021, 39(9): 100-108
LSTM模型在耕地面积预测领域的构建与应用
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向雁1 , 侯艳林1 , 姜文来2 , 陈印军2 , 成良强3
作者信息
1. 贵州商学院旅游管理学院, 贵阳 550014;
通讯作者:
姜文来(通信作者),研究员,研究方向为农业资源管理与利用,电子信箱:jiangwenlai@caas.cn
Establishment and application of LSTM model for cultivated land area prediction
Affiliations
出版时间: 2021-05-13
doi: 10.3981/j.issn.1000-7857.2021.09.012
文章导航
长短期记忆（LSTM）模型广泛应用于系统故障、交通流量、股票指数、紧急事件、碳排放、石油产量、农区地下水位等多个领域，均表现了出色的预测性能。为了丰富耕地面积预测方法、提升耕地预测精度，将LSTM模型引入耕地面积预测。选择常用的趋势外推模型、指数平滑模型、灰色模型、移动平均自回归、支持向量机、NAR动态神经网络等6类模型进行对比，并以耕地变化趋势比较复杂的黑龙江省和变化趋势比较单一的辽宁省、吉林省作为案例进行分析，以验证LSTM模型耕地面积预测效果。结果表明，从均方根误差（RMSE）、平均绝对误差（MAPE）这2个指标的综合评价来看， LSTM模型拟合和预测效果均为最优。根据LSTM模型预测， 2018—2030年黑龙江、吉林、辽宁3省的耕地面积将呈持续减少的趋势，耕地减少速度均有放缓之势。
耕地
/
预测模型
/
深度学习
/
神经网络
/
LSTM模型
The long-short term memory model (LSTM) is a special recurrent neural network structure, which is widely used in system failure, traffic flow, stock index, emergency event, carbon emission, water table depth, and other fields, showing excellent prediction performance. This paper introduces the LSTM model into forecasting cultivated land area to enrich predicting methods and improve prediction accuracy. To verify the validity of the LSTM model in cultivated land area prediction, TE, GM, ES, ARIMA, SVM and NARNET models are selected for comparison, in which Heilongjiang, Jilin and Liaoning provinces are taken as case areas for revealing evaluation effects of different time series models. The results indicate that the prediction effect of LSTM is better than other models in terms of the comprehensive evaluation of RMSE and MAPE. Finally, according to LSTM forecast, the cultivated land areas of Heilongjiang, Jilin and Liaoning provinces will continue to decrease from 2018 to 2030 and the decrease rate will slow down.
cultivated land
/
forecast model
/
deep learning
/
neural network
/
LSTM
向雁, 侯艳林, 姜文来, 陈印军, 成良强.
LSTM模型在耕地面积预测领域的构建与应用.
科技导报,
2021
, 39
(9)
: 100
-108
.
DOI: 10.3981/j.issn.1000-7857.2021.09.012
XIANG Yan, HOU Yanlin, JIANG Wenlai, CHEN Yinjun, CHENG Liangqiang.
Establishment and application of LSTM model for cultivated land area prediction[J].
Science & Technology Review ,
2021
, 39
(9)
: 100
-108
.
DOI: 10.3981/j.issn.1000-7857.2021.09.012
2021年第39卷第9期
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文章信息
doi: 10.3981/j.issn.1000-7857.2021.09.012
接收时间：2020-08-20
首发时间：2021-06-08
出版时间：2021-05-13
收稿日期：2020-08-20
修回日期：2020-11-05
通讯作者:
姜文来(通信作者),研究员,研究方向为农业资源管理与利用,电子信箱:jiangwenlai@caas.cn
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2021.09.012
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2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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