“水文预报”是个多义词，它可以指水文预报（2006年中国水利水电出版社出版书籍）, 水文预报（水文情况预测技术）。
Microsoft Word - 07.doc 关键词 ：神经网络；水文预报；水信息技术 [gap=9689]Key words: artificial neural network; hydrological forecast; hydro informatics
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水文预报 (Hydrologic Forecasting)：根据前期或现时已出现的水文、气象等信息，运用水文学、气象学、水力学的原理和方法，对河流、湖泊等水体未来一定时段内的水文...
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... hydroglider 滑行艇 hydrograph forecast 水文预报 hydrograph 自记水位计;流量图;水位图;水文图;水道测量图 ...
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... 水文预报 hydrological forecast 水文预报 hydrolgocial forecast 预见期 leading time ...
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短期水文预报 short term hydrological forecasting ; short date hydrologic forecasting
中期水文预报 medium-term hydrological forecast
长期水文预报 long-term hydrological forecast ; long-term hydrologic forecasting
水文预报服务 AHPS
海洋水文预报 [海洋] marine hydrologic forecasting
中、长期水文预报 mid and long term hydrological forecast
水库水文预报 hydrologic forecasting of reservoir
概率水文预报 Bayesian probabilistic forecast
The influential factors of hydrological forecast results are mainly hydrology, meteorology, geography and geology as well as other uncertain factors. They all bear certain randomness.
但由于水文预报的结果影响因子主要表现为水文、气象、地理地质等不确定性因素,这些因素都具有一定的随机性。
参考来源 - 动态贝叶斯网络在水文预报中的应用Firstly, forecast factors are selected to constitute the long-term forecast database based on the forecast object considering the characteristics of hydrology forecast.
首先,结合中长期水文预报的特殊性,根据预报目标初选物理影响因子。
参考来源 - 中长期水文预报及其在平原洪水资源利用中的应用研究From analysis, the SMG model with decaying recursion updating is the most suitable model for reservoir 10 day runoff forecast at Liujiaxia and Longyangxia. And neural network model is the next.
通过对比分析，认为加衰减递推实时修正的 SMG模型是最适合黄河上游的中长期水文预报模型，神经网络次之。
参考来源 - 黄河上游龙、刘两库汛期入库径流量中期预报方法比较研究Parameter’s calibrations of watershed hydrologic forecasting models and flood real-time correction are very important and difficult jobs.
流域水文预报模型的参数率定和实时洪水校正是洪水预报中非常重要和困难的工作。
参考来源 - 智能算法在流域洪水预报系统建模中的应用及其软件集成体系According to above aspects, the paper achieved valuable applications combined with ANNs’ applications in hydrology prediction and load forecasting. The major research work is outlined as follows.
本论文正是基于以上三个方面的考虑，结合人工神经网络技术在水文预报及电力系统负荷预测中的应用，进行了较系统的探索，取得了以下具有创新意义的研究成果。
参考来源 - 人工神经网络技术及其应用According to above aspects, the paper achieved valuable applications combined with ANNs’ applications in hydrology prediction and load forecasting. The major research work is outlined as follows.
本论文正是基于以上三个方面的考虑，结合人工神经网络技术在水文预报及电力系统负荷预测中的应用，进行了较系统的探索，取得了以下具有创新意义的研究成果。
参考来源 - 人工神经网络技术及其应用·2,447,543篇论文数据，部分数据来源于NoteExpress
流域水文预报模型的建立。
周期迭加预报是中长期水文预报的一种实用模型。
Period superposition forecasting model is a practical method in mid-to-long-term hydrological forecasting.
水文和水文气象数据的实时变分同化进入业务水文预报。
Real-time variational assimilation of hydrologic and hydrometeorological data into operational hydrologic forecasting.
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