Orientador: Jefferson Nascimento de Oliveira / Resumo: O estudo do comportamento de corpos hídricos propicia a obtenção de informações relevantes à bacia hidrográfica e ao meio pertencente a ela, resultando em diversos benefícios, não só do ponto de vista econômico, onde através de operação otimizada de reservatórios de água pode-se obter um maior aproveitamento para geração de energia hidrelétrica, abastecimento, irrigação ou outros fins, mas também subsidia o planejamento e a tomada de decisões com a relação à mitigação de riscos hidrodinâmicos, como o mapeamento de zonas potencialmente inundáveis e a detecção de áreas sujeitas a maiores riscos relacionados às inundações. Neste trabalho foi desenvolvido um modelo operacional simplificado para propagação de vazões, baseado no modelo Muskingum-Cunge-Todini, e realizado comparações dos resultados com o modelo hidrodinâmico completo HEC-RAS. Os modelos foram aplicados em trechos do rio Tocantins, entre os municípios de Peixe –TO e Bom Jesus do Tocantins–TO. Os resultados obtidos apresentaram similaridade entre o HEC-RAS e o modelo desenvolvido. / Abstract: The study of the behavior of water bodies supports relevant information to the hydrographic basin and to the means belonging to it, resulting in several benefits. Not only from the economic point of view, where, through an optimized operation of water reservoirs, can be obtained greater use for hydroelectric power generation, water supply, irrigation, or other purposes, but subsidizes planning and decision-making about hydrodynamic risk mitigation, such as mapping potentially floodable areas and the detection of areas subjected to higher risks of floods. In this work a simplified operating model for flow propagation was developed, based on the Muskingum-Cunge-Todini model, and comparisons of the results with the complete hydrodynamic model HEC-RAS were performed. The models were applied in stretches of the Tocantins river, between the municipalities of Peixe – and Bom Jesus do Tocantins-TO. The results obtained showed similarity between HEC-RAS and the developed model. / Mestre

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第19976号 / 工博第4220号 / 新制||工||1653(附属図書館) / 33072 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 立川 康人, 教授 堀 智晴, 准教授 KIM SUNMIN / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

A modelagem hidrológica quando associada aos recursos do sensoriamento remoto e do geoprocessamento torna-se uma ferramenta importante, pois é capaz de estabelecer diferentes cenários da cobertura e do uso da terra e suas implicações na drenagem urbana, auxiliando no planejamento urbano. Entretanto, a relação entre o modelo chuva x vazão e tais técnicas, com finalidade de avaliar classificadores de imagens a partir de hidrogramas de cheia não foi encontrada na literatura, tornando esse o objetivo principal desta tese. Para isso, foram utilizadas três imagens de satélite de diferentes resoluções espaciais (0,5m, 5m e 15m) e três algoritmos classificadores (Máxima Verossimilhança, Máquinas Vetores Suporte e Análise Orientada a Objeto) e formados conjuntos denominado \"classificador-imagem\" para classificação da cobertura e do uso da terra. As áreas das classes dos usos da terra de cada conjunto \"classificador-imagem\" e os valores de Curve Number foram os principais dados de entrada do modelo chuva-vazão NRCS, que permitiu gerar os hidrogramas de cheia para cada caso. Os hidrogramas simulados foram comparados aos hidrogramas observados na bacia e avaliados, quanto a sua representatividade, pelo coeficiente de Nash Sutcliffe. As classificações do uso da terra foram avaliadas pelo Índice Kappa, com valores de 0,58 a 0,99 e pela Exatidão Global, com valores de 0,64 a 0,99. Para as vazões, o coeficiente de Nash Sutcliffe foi considerado satisfatório (NS<0,50) em duas simulações e, nas demais simulações, considerado muito bom (NS>0,75). Para fornecer subsídio a tomada de decisão, foi realizada uma análise multicritério dos conjuntos classificador-imagem, que permitiu classificar os conjuntos com maior desempenho: 1°) o classificador SVM e a imagem Landsat-8; 2°) o classificador MaxVer e a imagem WordView-II; 3°) o classificador NN e a imagem RapidEye. / Hydrological modeling when associated with remote sensing and geoprocessing resources becomes an important tool, because it is able to establish different land use scenarios and its implications for urban drainage, assisting in urban planning. However, the relationship between the routing model and such techniques, for purpose to evaluate images classifiers from the runoff hydrograph was not found in the literature, making this the main objective of this thesis. Thereunto, three satellite images were used in different spatial resolutions (0.5m, 5m and 15m) and three algorithms classifiers (Maximum Likelihood, Support Vector Machine and Oriented Object Analysis) and composed sets called \"classifier-image\" for the land use classification. The areas of the land use classes of each set \"classifier-image\" and the Curve Number values were the main input of the routing model NRCS, which allowed generating the runoff hydrograph for each case. The simulated hydrographs were compared to the observed hydrograph in the basin and evaluated their representativeness through the Nash Sutcliffe coefficient. Kappa Index was calculated to evaluate land use classifications, with values between 0.58 to 0.99 and Global accuracy between 0.64 to 0.99. Towards the flows rates, the Nash Sutcliffe coefficient was considered satisfactory for two simulations (NS<0,50) and, to other simulations, considered very good (NS>0,75). To provide subsidy to decision-making, it carried out a multi-criteria analysis of the classifier-image sets, that allowed to classify the set with higher performance: 1) SVM classifier and Landsat-8 image; 2) MaxVer classifier and WorldView-II image; 3) NN classifier and RapidEye image.

The complex interaction between flow, vegetation and sediment drives the never settled changes of riverine system. Vegetation intercepts rainfall, adds resistance to surface flow, and facilitates infiltration. The magnitude and timing of flood flow are closely related to the watershed vegetation coverage. In the meantime, flood flow can transport a large amount of sediment resulting in bank erosion, channel degradation, and channel pattern change. As climate changes, future flood frequency will change with more intense rainfalls. However, the quantitative simulation of flood flow in vegetated channel and the influence of climate change on flood frequency, especially for the arid and semi-arid Southwest, remain challenges to engineers and scientists. Therefore, this research consists of two main parts: simulate unsteady flow and sediment transport in vegetated channel network, and quantify the impacts of climate change on flood frequency. A one-dimensional model for simulating flood routing and sediment transport over mobile alluvium in a vegetated channel network was developed. The modified St. Venant equations together with the governing equations for suspended sediment and bed load transport were solved simultaneously to obtain flow properties and sediment transport rate. The Godunov-type finite volume method is employed to discretize the governing equations. Then, the Exner equation was solved for bed elevation change. Since sediment transport is non-equilibrium when bed is degrading or aggrading, a recovery coefficient for suspended sediment and an adaptation length for bed load transport were used to quantify the differences between equilibrium and non-equilibrium sediment transport rate. The influence of vegetation on floodplain and main channel was accounted for by adjusting resistance terms in the momentum equations for flow field. A procedure to separate the grain resistance from the total resistance was proposed and implemented to calculate sediment transport rate. The model was tested by a flume experiment case and an unprecedented flood event occurred in the Santa Cruz River, Tucson, Arizona, in July 2006. Simulated results of flow discharge and bed elevation changes showed satisfactory agreements with the measurements. The impacts of vegetation density on sediment transport and significance of non-equilibrium sediment transport model were accounted for by the model. The two-dimensional surface flow model, called CHRE2D, was improved by considering the vegetation influence and then applied to Santa Cruz River Watershed (SCRW) in the Southern Arizona. The parameters in the CHRE2D model were calibrated by using the rainfall event in July 15th, 1999. Hourly precipitation data from a Regional Climate Model (RCM) called Weather Research and Forecasting model (WRF), for three periods, 1990-2000, 2031-2040 and 2071-2079, were used to quantify the impact of climate change on the magnitude and frequency of flood for the Santa Cruz River Watershed (SCRW) in the Southern Arizona. Precipitation outputs from RCM-WRF model were bias-corrected using observed gridded precipitation data for three periods before directly used in the watershed model. The watershed model was calibrated using the rainfall event in July 15th, 1999. The calibrated watershed model was applied to SCRW to simulate surface flow routing for the selected three periods. Simulated annual and daily maximum discharges are analyzed to obtain future flood frequency curves. Results indicate that flood discharges for different return periods are increased: the discharges of 100-year and 200-year return period are increased by 3,000 and 5,000 cfs, respectively.

A modelagem hidrológica quando associada aos recursos do sensoriamento remoto e do geoprocessamento torna-se uma ferramenta importante, pois é capaz de estabelecer diferentes cenários da cobertura e do uso da terra e suas implicações na drenagem urbana, auxiliando no planejamento urbano. Entretanto, a relação entre o modelo chuva x vazão e tais técnicas, com finalidade de avaliar classificadores de imagens a partir de hidrogramas de cheia não foi encontrada na literatura, tornando esse o objetivo principal desta tese. Para isso, foram utilizadas três imagens de satélite de diferentes resoluções espaciais (0,5m, 5m e 15m) e três algoritmos classificadores (Máxima Verossimilhança, Máquinas Vetores Suporte e Análise Orientada a Objeto) e formados conjuntos denominado \"classificador-imagem\" para classificação da cobertura e do uso da terra. As áreas das classes dos usos da terra de cada conjunto \"classificador-imagem\" e os valores de Curve Number foram os principais dados de entrada do modelo chuva-vazão NRCS, que permitiu gerar os hidrogramas de cheia para cada caso. Os hidrogramas simulados foram comparados aos hidrogramas observados na bacia e avaliados, quanto a sua representatividade, pelo coeficiente de Nash Sutcliffe. As classificações do uso da terra foram avaliadas pelo Índice Kappa, com valores de 0,58 a 0,99 e pela Exatidão Global, com valores de 0,64 a 0,99. Para as vazões, o coeficiente de Nash Sutcliffe foi considerado satisfatório (NS<0,50) em duas simulações e, nas demais simulações, considerado muito bom (NS>0,75). Para fornecer subsídio a tomada de decisão, foi realizada uma análise multicritério dos conjuntos classificador-imagem, que permitiu classificar os conjuntos com maior desempenho: 1°) o classificador SVM e a imagem Landsat-8; 2°) o classificador MaxVer e a imagem WordView-II; 3°) o classificador NN e a imagem RapidEye. / Hydrological modeling when associated with remote sensing and geoprocessing resources becomes an important tool, because it is able to establish different land use scenarios and its implications for urban drainage, assisting in urban planning. However, the relationship between the routing model and such techniques, for purpose to evaluate images classifiers from the runoff hydrograph was not found in the literature, making this the main objective of this thesis. Thereunto, three satellite images were used in different spatial resolutions (0.5m, 5m and 15m) and three algorithms classifiers (Maximum Likelihood, Support Vector Machine and Oriented Object Analysis) and composed sets called \"classifier-image\" for the land use classification. The areas of the land use classes of each set \"classifier-image\" and the Curve Number values were the main input of the routing model NRCS, which allowed generating the runoff hydrograph for each case. The simulated hydrographs were compared to the observed hydrograph in the basin and evaluated their representativeness through the Nash Sutcliffe coefficient. Kappa Index was calculated to evaluate land use classifications, with values between 0.58 to 0.99 and Global accuracy between 0.64 to 0.99. Towards the flows rates, the Nash Sutcliffe coefficient was considered satisfactory for two simulations (NS<0,50) and, to other simulations, considered very good (NS>0,75). To provide subsidy to decision-making, it carried out a multi-criteria analysis of the classifier-image sets, that allowed to classify the set with higher performance: 1) SVM classifier and Landsat-8 image; 2) MaxVer classifier and WorldView-II image; 3) NN classifier and RapidEye image.

The dynamic wave model (the complete form of the saint-Venant equations), as applied to flow routing in irrigation canals or flood routing in natural channels, is associated with parameter and model uncertainties. The parameter uncertainty arises due to imprecision in the estimation of Manning’s n used for calculating the friction slope (sf) in the momentum equation of the dynamic wave model. Accurate estimation of n is difficult due to its dependence on several channel and flow characteristics. The model uncertainty of the dynamic wave model arises due to difficulty in applying the momentum equation to curved channels, as it is a vector equation. The one-dimensional form of the momentum equation is derived assuming that the longitudinal axis of the channel is a straight line, so that the net force vector is equal to the algebraic sum of the forces involved. Curved channel reaches have to be discretized into small straight sub-reaches while applying the momentum equation. Otherwise, two- or three-dimensional forms of the momentum equation need to be adopted. A main objective of the study presented in the thesis is to develop a fuzzy dynamic wave model (FDWM), which is capable of overcoming the parameter and model uncertainties of the dynamic wave model mentioned above, specifically for problems of flow routing in irrigation canals and flood routing in natural channels. It has been demonstrated earlier in literature that the problem of parameter uncertainty in infiltration models can be addressed by replacing the momentum equation by a fuzzy rule based model while retaining the continuity equation in its complete form. The FDWM is developed by adopting the same methodology: i.e. By replacing the momentum equation of the dynamic wave model by a fuzzy rule based model while retaining the continuity equation in its complete form. The fuzzy rule based model is developed based on fuzzification of a new equation for wave velocity, to account for the model uncertainty and backwater effects. A fuzzy dynamic wave routing model (FDWRM) is developed based on application of the FDWM to flow routing in irrigation canals. The fuzzy rule based model is developed based on the observation that inertia dominated gravity wave predominates in irrigation canal flows. Development of the FDWRM and the method of computation are explained. The FDWRM is tested by applying it to cases of hypothetical flow routing in a wide rectangular channel and also to a real case of flow routing in a field canal. For the cases of hypothetical flow routing in the wide rectangular channel, the FDWRM results match well with those of an implicit numerical model (INM), which solves the dynamic wave model; but the accuracy of the results reduces with increase in backwater effects. For the case of flow routing in the field canal, the FDWRM outputs match well with measured data and also are much better than those of the INM. A fuzzy dynamic flood routing model (FDFRM) is developed based on application of the FDWM to flood routing in natural channels. The fuzzy rule based model is developed based on the observation that monoclinal waves prevail during floods in natural channels. The natural channel reach is discredited into a number of approximately uniform sub-reaches and the fuzzy rule based model for each sub-reach is obtained using the discharge (q)–area (a) relationship at its mean section, based on the kleitz-seddon principle. Development of the FDFRM and the method of computation are explained. The FDFRM is tested by applying it to cases of flood routing in fictitious channels and to flood routing in a natural channel, which is described in the HEC-RAS (hydrologic engineering center – river analysis system) application guide. For the cases of flood routing in the fictitious channels, the FDFRM outputs match well with the INM results. For the case of flood routing in the natural channel, optimized fuzzy rule based models are derived using a neuro-fuzzy algorithm, to take the heterogeneity of the channel sub-reaches into account. The resulting FDFRM outputs are found to be comparable to the HEC-RAS outputs. Also, in literature, the dynamic wave model has been applied in the inverse direction for the development of centralized control algorithms for irrigation canals. In the present study, a centralized control algorithm based on inversion of the fuzzy dynamic wave model (FDWM) is developed to overcome the drawbacks of the existing centralized control algorithms. A fuzzy logic based dynamic wave model inversion algorithm (FDWMIA) is developed for this purpose, based on the inversion of the FDWM. The FDWMIA is tested by applying it to two canal control problems reported in literature: the first problem deals with water level control in a fictitious canal with a single pool and the second, with water level control in a real canal with a series of pools (ASCE Test Canal 2). In both cases, the FDWMIA results are comparable to those of the existing centralized control algorithms.

The multi-net Global Routing Problem (GRP) in VLSI physical design is a problem of routing a set of nets subject to limited resources and delay constraints. Various state-of-the-art routers are available but their main focus is to optimize the wire length and minimize the over ow. However optimizing wire length do not necessarily meet timing constraints at the sink nodes. Also, in modern nano-meter scale VLSI process the consideration of process variations is a necessity for ensuring reasonable yield at the fab. In this work, we try to nd a fundamental strategy to address the timing-driven Steiner tree construction (i.e., the routing) problem subject to congestion constraints and process variation. For congestion mitigation, a gradient based concurrent approach (over all nets) of Erzin et. al., rather than the traditional (sequential) rip-and-reroute is adopted in or- der to propagate the timing/delay-driven property of the Steiner tree candidates. The existing sequential rip-up and reroute methods meet the over ow constraint locally but cannot propagate the timing constraint which is non-local in nature. We build on this approach to accommodate the variation-aware statistical delay/timing requirements. To further reduce the congestion, the cost function of the tree generation method is updated by adding history based congestion penalty to the base cost (delay). Iterative use of the timing-driven Steiner tree construction method and history based tree construction procedure generate a diverse pool of candidate Steiner trees for each net. The gradient algorithm picks one tree for each net from the pool of trees such that congestion is e ciently controlled. As the technology scales down, process variation makes process dependent param- eters like resistance, capacitance etc non-deterministic. As a result, Statistical Static Timing Analysis or SSTA has replaced the traditional static timing in nano-meter scale VLSI processes. However, this poses a challenge regarding the max/min-plus algebra of Dijkstra like approximation algorithm that builds the Steiner trees. A new approach based on distance between distributions for nding maximum/minimum at the nodes is presented in this thesis. Under this metric, the approximation algorithm for variation aware timing driven congestion constrained routing is shown to be provably tight and one order of magnitude faster than existing approaches (which are not tight) such as the MVERT. The results (mean value) of our variation aware router are quite close to the mean of the several thousand Monte Carlo simulations of the deterministic router, i.e the results converge in mean. Therefore, instead of running so many deterministic Monte Carlo simulations, we can generate an average design with a probability distribution reasonably close to that of the actual behaviour of the design by running the proposed statistical router only once and at a small fraction of the computational e ort involved in physical design in the nano regime VLSI. The above approximation algorithm is extended to local routing, especially non- Manhattan lambda routing which is increasingly being allowed by the recent VLSI tech- nology nodes. Here also, we can meet delay driven constraints better and keep related wire lengths reasonable.