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	联合EEMD与BP神经网络的灌区水源情势预测研究
其他题名	Predicting Change in Water Availability for Jinghui Irrigation District Using Ensemble Empirical Mode Decomposition and Back Propagation Neural Network
	李雯晴; 刘招; 王丽霞; 李强; 吴小宏
来源期刊	灌溉排水学报
ISSN	1672-3317
出版年	2020
卷号	39 期号:10 页码:108-114
中文摘要	泾河作为泾惠渠灌区渠首地表水源,来水逐年减少。【目的】分析探讨泾惠渠灌区渠首水源形势,保障灌区水资源管理及粮食生产安全。【方法】研究建立了基于E EMD-BP的水文序列预测模型,通过对模型进行训练和校正,最终预测了未来十年(2018-2027年)泾河来水来沙形势,分析了渠首的水资源可利用量。【结果】将EEMD与BP神经网络二者结合,可有效发挥各自优势,验证期各项评估指标也较为理想;灌区渠首水沙变化较为同步,在未来一定时期二者趋势均为短暂的上升后再下降,年均径流量约为11.87亿m3,较当前略有上升,年均输沙量约为1亿t,较当前略有下降,预测结果延续了泾河水沙多年变化的大致趋势;灌区渠首可利用水资源量相对比较平稳,平均水资源可利用量约为7.79亿m~3,可满足引水灌溉需求,但应注意一些干旱年份水资源的调蓄利用,谨防发生灌溉水短缺问题。【结论】验证表明模型误差在可接受范围内,且预测系列的变化趋势与当前实测系列完全一致,建立的EEMD-BP模型预测年径流及泥沙效果良好,预测结果具有较高的可信度。
英文摘要	【Background】Climate change and intensification of human activities have impacted hydrological cycling, resulting in a sharp decline in river inflow, frequent occurrence of extreme weathers and decline of groundwater table. The change in water resources has led to a great challenge to water resource management for both industry and agriculture, especially the massive irrigated areas in arid and semi-arid areas which rely on water diverted from rivers for irrigation. It is urgent to improve our understanding of potential change in water resource and irrigation demand to safeguard food production in these regions.【Objective】The inflow to the Jing river has been in decline and the available water for diverting to Jinhui irrigation district has been reduced as a result. The purpose of this paper is to analyze the factors affecting the change in water resources at the head of Jinghui Irrigation district in Zhangjiashan and predict its potential change. 【Method】A model based on the ensemble empirical mode decomposition (EEMD) and the back propagation (BP) artificial neural network was established. The measured hydrological data was used to train and correct the model prior to being used to predict runoff and sediment yield in the next 10 years from 2018 to 2027 at Zhangjiashan. In the meantime, we also analyzed the available surface water resources for the irrigation district.【Result】①The combination of the EEMD and the BP neural network could take the most of each to predict the nonstationary time series of the hydrological data. ②The change in water runoff and sediment yield at the head of the canal of the irrigation district was relatively consistent and simultaneous, both rising first followed by a decline in the next 10 years from 2018 to 2027. ③The average annual runoff will be approximately 1.186 billion m~3, slightly higher than the average over 1996-2017, while the average annual sediment yield will be about 100 million tones, slightly lower than the average over 1996-2017. ④The available water resources at Zhangjiashan from 2019 to 2027 will be relatively stable, approximately 779 million m~3 annually, slightly higher than the average over 1996 to 2017, and can meet the demand of the irrigation district for water diversion at current level. But management is required to ensure enough water will be able in dry years.【Conclusion】The errors of the model were in acceptable range, and the predicted results matched well with the measured data. The EEMD-BP model is accurate and reliable for predicting annual runoff and sediment yield, and it thus provides an alternative to help improve efficient utilization and management of water resources so as to safeguard agricultural production in the studied irrigation district.
中文关键词	水资源 ; 年径流量 ; 预测 ; 水沙规律 ; 灌区
英文关键词	water resources annual runoff forecasting nature of runoff and sediment irrigation district
类型	Article
语种	中文
收录类别	CSCD
WOS类目	Agriculture
CSCD记录号	CSCD:6842951
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/353583
作者单位	李雯晴, 长安大学水利与环境学院;;长安大学, ;;旱区地下水文与生态效应教育部重点实验室, 西安;;西安, ;; 710054;;710054. 刘招, 长安大学;;长安大学水与发展研究院, 旱区地下水文与生态效应教育部重点实验室;;, 西安;;西安, ;; 710054;;710054. 王丽霞, 长安大学地质工程与测绘学院, 西安, 陕西 710054, 中国. 李强, 长安大学水利与环境学院, 西安, 陕西 710054, 中国. 吴小宏, 泾惠渠灌溉管理局, 三原, 陕西 713800, 中国.
推荐引用方式 GB/T 7714	李雯晴,刘招,王丽霞,等. 联合EEMD与BP神经网络的灌区水源情势预测研究[J],2020,39(10):108-114.
APA	李雯晴,刘招,王丽霞,李强,&吴小宏.(2020).联合EEMD与BP神经网络的灌区水源情势预测研究.灌溉排水学报,39(10),108-114.
MLA	李雯晴,et al."联合EEMD与BP神经网络的灌区水源情势预测研究".灌溉排水学报 39.10(2020):108-114.