超前洪水预估精度不高一直困扰防洪决策，为解决这一技术难题，提出一种基于暴雨洪水知识的相似性分 析方法，进行洪水预报预测。该方法从历史典型暴雨洪水知识中提取多要素特征指标，基于欧氏距离进行当前与 历史暴雨洪水的特征指标相似性判别，根据判别出的最相似洪水，经“峰-量”联合修正消除非一致性后，实时外推 预估未来洪水过程，构成一套完整的“多要素特征指标提取-历史暴雨洪水相似性判别-实时洪水修正外推预估” 技术。在沂河蒙阴站的应用结果表明，基于“降雨-径流”关系，对判别出的最相似洪水进行修正，显著提高了外推 预估洪水精度，洪峰流量相对误差δQm 的范围降至 10?% 附近，峰现时间绝对误差 ΔT的范围降至±2?h 以内，径流深 相对误差 δR的范围降至±20?% 以内，且随着时间推移，精度水平不断提高。该套技术方法能够挖掘隐含在历史暴 雨洪水数据中的相似性，超前预估当前洪水变化过程，为洪水预报提供一种新的技术参考。

Flood?forecasting?and?prediction?are?integral?components?of?non-structural?flood?management?measures. Methods ?for ?flood ?forecasting ?and ?prediction ?can ?generally ?be ?classified ?into ?two ?categories: ?process-driven approaches?(hydrological?models)?and?data-driven?approaches.?Traditionally,?the?focus?has?been?on?process-driven approaches,?but?with?the?accumulation?of?hydrological?data?and?advancements?in?big?data?analytics,?data-driven approaches?have?gained?increasing?attention.?In?particular,?the?application?of?artificial?intelligence?technology?in?the water?industry?has?led?to?the?emergence?of?hydrological?data?mining-based?forecasting?and?prediction?methods?as?a research?hotspot.?Conducting?hydrological?knowledge?mining?and?prediction?based?on?the?principle?of?similarity?has become?an?important?research?direction,?offering?a?new?technical?means?to?uncover?hidden?patterns?within?rainfall, floods,?and?watershed?surface?information.?This?approach?also?promotes?the?automation?and?intelligence?of?water resources ?data ?processing, ?assisting ?in ?improving ?the ?accuracy ?of ?flood ?forecasting ?and ?prediction, ?thereby facilitating?the?modernization?and?precision?of?the?water?industry. ??????In?theory,?the?longer?the?series?of?hydrological?data,?the?more?torrential?rain-induced?flood?knowledge?can?be extracted.?However,?hydrological?data?series?in?a?changing?environment?often?exhibit?inconsistencies,?which?affect the ?accuracy ?of ?flood ?forecasting ?and ?prediction ?based ?on ?torrential ?rain-induced ?flood ?knowledge. ?Currently, research?on?historical?similar?torrential?rain-induced?flood?knowledge?considering?inconsistencies?in?guiding?real- time?flood?forecasting?is?relatively?limited.?In?this?context,?a?methodology?based?on?the?knowledge?of?torrential?rain- induced?floods?for?real-time?flood?forecasting?and?prediction?is?proposed.?The?proposed?method?focuses?on?historical records?of?typical?torrential?rain-induced?floods?and?extracts?rainfall?feature?indicators,?such?as?the?position?of?the rainstorm?center,?antecedent?precipitation,?total?average?rainfall,?and?rainfall?processes.?Multiple?feature?indicators are?simultaneously?assessed?for?their?similarity?using?criteria?such?as?Euclidean?distance.?By?inferring?historical typical ?floods ?based ?on ?similarity ?knowledge ?and ?incorporating ?the ?"rainfall-peak ?flow" ?or ?"rainfall-runoff" relationship ?before ?and ?after ?the ?change, ?a ?combined ?"peak-flow" ?correction ?approach ?is ?applied ?to ?ensure consistency. ?Real-time ?rolling ?extrapolation ?is ?then ?performed ?to ?estimate ?future ?flood ?processes, ?forming ?a comprehensive?"multi-feature?indicator?extraction-historical?torrential?rain-induced?flood?similarity?determination- real-time?flood?correction?and?extrapolation"?technique. ??????The?application?results?at?the?Mengyin?Station?on?the?Yi?River?demonstrate?the?effectiveness?of?the?proposed methodology.?For?any?given?torrential?rain-induced?flood?event,?the?most?similar?historical?flood?event?can?be accurately?identified?through?multiple?feature?indicators,?ensuring?the?theoretical?correctness?of?the?technique.?By considering?the?most?identified?similar?flood?event?and?applying?suitable?corrections?to?ensure?consistency,?the extrapolation ?and ?prediction ?of ?future ?flood ?processes ?significantly ?improve ?the ?accuracy ?of ?real-time ?flood forecasting?compared?to?the?direct?application?of?similar?flood?processes. ??????In?summary,?the?suggested?methodology,?grounded?in?torrential?rain-induced?flood?knowledge,?introduces?an effective?avenue?for?real-time?flood?forecasting?and?prediction.?By?extracting?multiple?feature?indicators,?evaluating their?similarity,?and?incorporating?correction?and?extrapolation?steps,?it?enables?accurate?identification?of?similar historical ?flood ?events ?and ?enhances ?the ?precision ?of ?real-time ?flood ?forecasting. ?This ?study ?contributes ?to ?the progression?of?flood?management?and?establishes?the?groundwork?for?further?research?aimed?at?enhancing?flood forecasting?accuracy?and?propelling?the?modernization?of?the?water?industry.