为解决因水库数据采集设备能力有限、水文数据不全导致预测水库水位时预测精度较低的问题，以四岭水 库每小时水位监测数据为例，提出基于嵌入式-门控循环单元（Embedding-gated?recurrent?unit，Embedding-GRU）的 水库水位预测模型，即利用 Embedding 方法将单维降雨量数据升维至多维数据，扩大降雨的气候特征，结合 GRU 算法进行水库水位预测。将该模型与传统深度学习算法长短期记忆（long?short-term?memory，LSTM）、门控循环单 元（gated?recurrent?unit，GRU）、双向门控循环单元（bidirectional?recurrent?neural?network，BiGRU）这 3 种模型对比， 结果显示：Embedding-GRU 模型的预测效果均优于其他传统模型，平均绝对误差 EMA和均方根误差 ERMS分别平均 下降 19.6% 和 7.7%，并且在预测次日水库水位的应用场景中决定系数 R2能够达到 0.989?37。结果表明：该模型耦 合多种算法，扩大单变量的气候特征，具有较高预测精度和泛化能力。相较传统模型，基于 Embedding-GRU 的水 库水位预测模型能够对缺少温度、气压、风速、蒸发量等监测数据的水库进行可靠度较高的预测，适用水库范围 更广，为水库日常运维、除险加固提供参考。

The?prediction?of?reservoir?water?level?is?of?great?significance?in?the?daily?operation?and?management?of reservoir,?the?reinforcement?of?dam,?the?mitigation?of?flood?disaster,?and?the?protection?of?people's?life?and?property safety. ?However, ?with ?the ?change ?of ?global ?temperature, ?the ?frequency ?of ?extreme ?weather ?increases ?and ?the uncertainty?of?abnormal?rainfall?increases,?which?lead?to?the?lagging?of?reservoir?level?prediction?methods?relying?on traditional?engineering?hydrology.?Due?to?the?high?practicability?of?deep?learning?algorithms?used?in?various?fields, there?are?a?few?examples?of?domestic?and?foreign?scholars?using?artificial?intelligence?to?predict?water?levels.?In order?to?make?up?for?the?shortcomings?of?single?artificial?intelligence?model,?some?scholars?also?used?the?neural network?model?coupling?method?to?study?water?level?prediction,?and?a?small?number?of?scholars?input?a?single variable?to?predict?water?level.?The?above?research?shows?that?it?is?feasible?to?use?the?coupled?model?for?water?level prediction,?and?the?advantages?of?multiple?models?complement?each?other,?and?the?prediction?accuracy?is?improved to?different?degrees?compared?with?the?previous?single?model. ??????Considering?various?practical?factors,?the?monitoring?data?of?water?level?of?Siling?Reservoir?was?taken?as?an example?and?the?coupling?prediction?model?of?water?level?of?reservoir?was?put?forward?based?on?Embedding?GRU on?the?condition?that?there?was?only?a?single?characteristic?rainfall,?in?order?to?provide?a?reference?for?realizing?the high-precision?prediction?of?water?level?with?single?characteristics.?According?to?the?rainfall?scale?of?the?data?set?and the?largest?rainfall?in?the?history?of?Zhejiang?Province,?the?training?parameter?rainfall?scale?sets?of?the?Embedding stage ?is ?defined ?as ?{500,550,600,650,700,750} ?with ?the ?accuracy ?of ?mm×10 ?1. ?In ?order ?to ?study ?the ?optimal parameter?setting,?the?range?of?feature?dimension?setting?was?extended?to?{2,3,4,5,6}?on?the?premise?of?adopting?the control?variates.?The?ERMS?indicator?was?selected?for?this?experiment.?To?further?validate?the?predictive?performance and?generalization?ability?of?the?Embedding?GRU?model,?an?experiment?was?conducted?based?on?the?total?daily rainfall?to?predict?the?next?day's?reservoir?water?level.?The?comparison?algorithm?is?still?LSTM,?GRU,?and?BiGRU, with?a?total?of?1?826?sets?of?data?with?a?data?volume?of?5?years. ??????Compared?with?other?existing?artificial?intelligence?models?of?reservoir?water?level,?the?prediction?accuracy?is higher?and?the?scope?of?reservoir?is?wider.?In?the?comparative?experiment?of?predicting?the?next?hour's?water?level，the ?prediction ?ability ?of ?the ?four ?models ?was ?excellent, ?and ?they ?could ?fit ?the ?real ?water ?level ?data ?relatively accurately, ?which ?shows ?that ?the ?method ?of ?predicting ?the ?reservoir ?water ?level ?by ?deep ?learning ?algorithm ?is effective?and?feasible.?By?comparing?of?prediction?accuracy?of?four?models，the?experiment?proved?that?GRU algorithm?is?better?than?LSTM?in?prediction?effect,?and?the?embedding?method?can?further?effectively?reduce?the prediction?error?and?improve?the?prediction?accuracy?of?the?model. ??????It?is?the?Embedding?method?that?enlarges?the?features?between?rainfall?and?climate,?coupled?with?lightweight deep ?learning ?algorithm ?GRU ?to ?predict ?reservoir ?water ?level. ?Conclusions ?are ?as ?follows: ?(1) ?The ?prediction accuracy?of?the?Embedding?GRU?model?is?obviously?better?than?that?of?LSTM,?GRU,?BiGRU?and?other?single?deep learning?models.?(2)?Embedding?parameters?in?the?Embedding?GRU?model?shall?be?determined?by?comparative?test according?to?the?actual?data?set.?(3)?The?Embedding-GRU?model?has?excellent?performance?in?predicting?different period?of?multiple?times?within?7?days,?and?has?good?prediction?effect?and?generalization?ability,?which?fully?proves the?effectiveness?of?the?model.