【摘要】：隨著洪水預(yù)報科學(xué)的發(fā)展,水文模型已被廣泛地用來解決包括水文水資源、環(huán)境和生態(tài)等社會和人類發(fā)展問題。南方濕潤地區(qū)雨洪配套資料豐富,在進(jìn)行流域水文模型研究時可做的工作較多,計(jì)算方法也比較成熟。而對于占我國領(lǐng)土面積52%的半干濕地區(qū),水文模型方面的工作做的較少,流域水文模型的使用在國內(nèi)外在半干濕地區(qū)都存在問題。雙超模型尤適用于半濕潤半干旱地區(qū),作為洪水預(yù)報首選模型,應(yīng)給予充分重視,而現(xiàn)階段對雙超模型參數(shù)敏感性以及率定的研究貧乏,不能為各級政府和防汛部門提供準(zhǔn)確的模型信息,難以滿足洪水預(yù)報參數(shù)率定的需求。本文以雙超產(chǎn)流模型為研究對象,對模型參數(shù)進(jìn)行敏感性分析,識別模型輸出響應(yīng)的重要影響參數(shù),減少模型參數(shù)率定過程中的盲目性,并且建立山西省小流域洪水分類參數(shù)率定系統(tǒng),為參數(shù)率定提供依據(jù),提高模型運(yùn)行的可靠性與預(yù)報精度。本文以山西省內(nèi)榆社、上靜游與婁煩3個水文站控制流域作為研究對象,選取各流域內(nèi)具有代表性的場次洪水進(jìn)行雙超模型參數(shù)敏感性分析。首先采用局部分析法,得出了雙超模型參數(shù)在不同流域、不同等級洪水及多個目標(biāo)函數(shù)下的敏感性與相關(guān)性情況,基于變異系數(shù)法確定模型參數(shù)的綜合敏感性系數(shù)。再采用優(yōu)化的LH-OAT法,通過對參數(shù)的定向改變,得出了雙超模型參數(shù)在不同流域、不同等級洪水及多個目標(biāo)函數(shù)下的敏感性與相關(guān)性情況,基于熵值法確定了模型參數(shù)的綜合敏感性系數(shù)。并將兩種方法所得結(jié)論進(jìn)行對比分析,研究表明:(1)由局部分析法得到模型參數(shù)綜合敏感性大小排序?yàn)镾rbα0Ksσ≈C,參數(shù)Sr、Ks、b、α0為敏感性參數(shù),C、σ為不敏感參數(shù),由全局分析法得模型參數(shù)綜合敏感性大小排序?yàn)镵sbSrα0σ≈c,參數(shù)Sr、Ks、b為敏感性參數(shù),參數(shù)α0為較敏感參數(shù),C、σ為不敏感參數(shù)。對比二者成果可知不同的研究方法的到模型參數(shù)的敏感性大小排序有所不同。但是對于參數(shù)敏感性分級,僅α0受到分析方法的影響,其他參數(shù)敏感性等級具有較好的穩(wěn)定性。(2)采用局部分析法與全局分析法對參數(shù)與目標(biāo)函數(shù)相關(guān)性分析可知,在不同等級洪水、不同流域中,各敏感性參數(shù)與目標(biāo)函數(shù)Wi、Qmi的相關(guān)性明確。表現(xiàn)為參數(shù)α0、b與Wi、Qmi正相關(guān),參數(shù)Sr、Ks與Wi、Qmi呈負(fù)相關(guān)。但各參數(shù)并不是對所有的目標(biāo)函數(shù)都有明確的相關(guān)性,當(dāng)目標(biāo)函數(shù)變?yōu)镮VF、RE、RSS、PE時,相關(guān)性不明確。因此,有在實(shí)際運(yùn)用中針對不同目標(biāo)函數(shù),在參數(shù)的調(diào)節(jié)方面需要區(qū)別對待,并不是都有規(guī)律可循。本文采用模糊ISOD ATA迭代模型對歷史洪水進(jìn)行聚類分析。因場次洪水過程的洪峰流量和洪水總量為洪水預(yù)報的主要目標(biāo),所以選定歷史洪水的洪峰流量和洪水總量為聚類特征指標(biāo)進(jìn)行聚類分析。將歷史洪水按照量級大小分為大洪水、中洪水、小洪水3種類型。由于洪水現(xiàn)象復(fù)雜多變,難以掌控,產(chǎn)匯流規(guī)律在不同類型洪水中也不盡相同,為降低僅用一組水文預(yù)報模型參數(shù)對全流域洪水進(jìn)行預(yù)報的誤差,本文確立了水文預(yù)報模型參數(shù)分類率定的思路,來尋找同類型洪水產(chǎn)匯流的規(guī)律。水文預(yù)報模型參數(shù)分類率定結(jié)果表明:(1)本文所建立BP神經(jīng)網(wǎng)絡(luò)分類模型,可準(zhǔn)確判斷流域洪水所屬類型,在樣本預(yù)測中精度達(dá)到100%。(2)本文所建立的流域洪水分類預(yù)報方法,將傳統(tǒng)洪水預(yù)報的洪量合格率從73%提高到了82%,洪量相對誤差從18.1%減少到了11.3%;洪峰合格率也從73%提高到了82%,洪峰相對誤差從16.4%減少到了14.6%。提高了研究流域整體預(yù)報精度,為研究流域?qū)崟r調(diào)度提供了可靠依據(jù)。前人曾對雙超模型參數(shù)采用傳統(tǒng)擾動分析法僅對模型參數(shù)進(jìn)行了敏感性分類,并未對模型參數(shù)敏感性系數(shù)進(jìn)行定量計(jì)算,本文通過對目標(biāo)函數(shù)賦權(quán),進(jìn)行模型綜合敏感性系數(shù)分析,對參數(shù)的敏感性進(jìn)行了客觀全面的分析,成果更加完善與可靠,對深入了解雙超模型產(chǎn)流機(jī)理、減少模型率定過程與提高模型模擬精度等具有深遠(yuǎn)的實(shí)際意義;本文所建立的BP神經(jīng)網(wǎng)絡(luò)分類模型可以較為精確的判斷流域洪水量級大小,對流域洪水分類可靠。另外,洪水分類及識別結(jié)果受洪水分類特征指標(biāo)選取的影響,是應(yīng)進(jìn)一步研究的問題。
[Abstract]:With the development of flood forecast science, the hydrological model has been widely used to solve the problems of social and human development, including hydrology, water resources, environment and ecology. The data of the rain and flood in the wet area of the south is rich, and more work can be done in the study of the hydrological model of the river basin, and the calculation method is also mature. However, for the semi-arid area, which is 52% of the territory of our country, the work of the hydrological model is less, and the use of the hydrological model in the basin is a problem both at home and abroad in the semi-arid area. The double supermodel is especially suitable for semi-humid and semi-arid areas, and should be given full attention as the first choice model of the flood forecast. At the present stage, the sensitivity and the rate of the double supermodel parameters are poor, and the accurate model information can not be provided for all levels of government and flood control departments. it is difficult to meet the demand of the flood forecast parameter rate. In this paper, a double super-production flow model is used as the research object, the sensitivity analysis of the model parameters is carried out, the important influence parameters of the output response of the model are identified, the blindness in the process of determining the model parameter rate is reduced, and the system for determining the flood classification parameter rate of the small watershed in Shanxi Province is established, and the reliability and the prediction precision of the model operation are improved. In this paper, the two-supermodel parameter sensitivity analysis of the representative field flood in each basin is selected as the object of the study on the control of the basin as the study object in Yulin, Shangjing and Lou. The sensitivity and correlation of the two supermodel parameters in different river basins, different grades of flood and multiple target functions are obtained firstly, and the comprehensive sensitivity coefficient of the model parameters is determined based on the coefficient of variation method. By using the optimized LH-OAT method, the sensitivity and the correlation of the two supermodel parameters under different river basins, different grade floods and multiple target functions are obtained, and the comprehensive sensitivity coefficient of the model parameters is determined based on the entropy value method. The results of the two methods are compared and analyzed. The results show that: (1) The comprehensive sensitivity of the model parameters is determined by the local analysis method as Srb-0Ks-C, the parameters Sr, Ks, b and {0} are sensitive parameters, and C and K are not sensitive parameters. The comprehensive sensitivity of the model parameters is determined by the global analysis method as the KsbSr-0-IGC, the parameters Sr, Ks and b are sensitive parameters, and the parameter {0} is the sensitive parameter, and the parameter C and the parameter are not sensitive parameters. The results show that the sensitivity and size of the model parameters are different from those of the different research methods. However, for the parameter sensitivity classification, only the sensitivity level of the other parameters is affected by the analysis method, and the other parameter sensitivity grades have good stability. (2) The correlation between the parameters and the objective function is analyzed by the local analysis method and the global analysis method, and the correlation between the sensitivity parameters and the target function Wi and Qmi is clear in different levels of flood and different river basins. The performance is that the parameters' 0, b 'are positively related to Wi and Qmi, and the parameters Sr, Ks are negatively correlated with Wi and Qmi. However, the parameters are not related to all target functions, and when the target function becomes IVF, RE, RSS, PE, the correlation is not clear. Therefore, there is a need to treat different target functions in the actual application, and the regulation of the parameters needs to be treated differently, and it is not all rules to follow. In this paper, the fuzzy ISOD ATA iterative model is used to cluster the historical flood. Since the flood peak flow and the flood volume of the field flood process are the main targets of the flood forecast, the flood peak flow and the total flood volume of the selected historical flood are cluster analysis. The historical flood is divided into three types of flood, medium flood and small flood according to the order of magnitude. Because the flood phenomenon is complicated and changeable, it is difficult to control, and the law of runoff generation is different in different types of flood. In order to reduce the error of forecasting the flood of the whole river basin by a group of hydrological forecasting model parameters, this paper establishes the idea of the classification rate of the parameter of the hydrological forecast model. so as to find the law of the same type of flood runoff and confluence. The classification rate of the model of the hydrological forecast model is as follows: (1) The classification model of the BP neural network is established in this paper, and the type of the basin flood can be accurately determined, and the accuracy of the model is 100% in the sample prediction. (2) The method of the watershed flood classification and forecast in this paper is to increase the qualified rate of the flood forecast from 73% to 82%, and the relative error of the flood volume from 18. 1% to 11. 3%. The qualification rate of the flood peak is also increased from 73% to 82%. The relative error of the flood peak was reduced from 16. 4% to 14. 6%. The whole forecast precision of the study basin is improved, and a reliable basis for studying the real-time dispatching of the river basin is provided. In this paper, the sensitivity classification of the model parameters is only carried out by the traditional perturbation analysis method, and the sensitivity coefficient of the model parameters is not calculated quantitatively, and the comprehensive sensitivity coefficient of the model is analyzed by the weight of the objective function. The sensitivity of the parameters is analyzed in an objective and comprehensive way, and the result is more perfect and reliable. It is of far-reaching significance to the deep understanding of the production flow mechanism of the double supermodel, the process of reducing the model rate and the improvement of the model precision, etc. The BP neural network classification model established in this paper can accurately judge the magnitude of the flood level of the river basin, and is reliable for the classification of the flood in the basin. In addition, the classification of the flood and the result of the identification are affected by the selection of the characteristics of the flood classification.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TV122

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