联合EEMD与BP神经网络的灌区水源情势预测研究
联合EEMD与BP神经网络的灌区水源情势预测研究
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LI, Wenqing , LIU, Zhao , WANG, Lixia , LI, Qiang and WU, Xiaohong
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Xinxiang City: Chinese Academy of Agricultural Sciences (CAAS) Farmland Irrigation Research Institute Editorial Office of Journal of Irrigation and Drainage
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Xinxiang City: Chinese Academy of Agricultural Sciences (CAAS) Farmland Irrigation Research Institute Editorial Office of Journal of Irrigation and Drainage
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S273; 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EEMD-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m3,可满足引水灌溉需求,但应注意一些...
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联合EEMD与BP神经网络的灌区水源情势预测研究
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TN_cdi_wanfang_journals_ggps202010015
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_wanfang_journals_ggps202010015
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ISSN
1672-3317
DOI
10.13522/j.cnki.ggps.2019121
