Teses em Engenharia Civil (Doutorado) - PPGEC/ITEC

URI Permanente para esta coleçãohttps://repositorio.ufpa.br/handle/2011/9887

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Avaliação de modelos de inteligência artificial híbridos na estimativa de precipitações
(Universidade Federal do Pará, 2022-03-18) GOMES, Evanice Pinheiro; BLANCO, Claudio José Cavalcante; http://lattes.cnpq.br/8319326553139808
The hydrological analyzes carried out from rainfall in the Amazon are essential due to its importance in climate regulation, regional and global atmospheric circulation. However, in this region, there are limitations related to data series with short periods and many flaws, especially in the daily scale. Despite significant advances in science and technology, practical and accurate predictions have been a major concern due to their complexity. Therefore, several conceptual models, empirical or hybrid, have been tested to forecast rain with greater precision. Among empirical models, those that incorporate artificial intelligence (AI) methods are potentially useful approaches to simulate the precipitation process. Artificial Neural Networks (ANN), as AI models, are able to establish a relationship between historical inputs (rain, flow, etc.) and the desired outputs, through a non-linear function composed of several factors that are adjusted to the observed data, allowing your prediction. Thus, to improve the precipitation analysis, hybrid models were developed, involving Artificial Neural Network (ANN) of the type with Time Delay (TDNN), ELMAN network, Radial Base network (RBF) and Adaptive Neuro-Fuzzy Inference System (ANFIS), coupled with Maximum Overlap Discrete Wavelet (MODWT). Six rainfall gauge station were adopted, which are located in different biomes of the region, and satellite data (CMORPH). Rainfall data were evaluated by seasonal periods (rainy and dry). The results obtained demonstrated that the MODWT-ANFIS model had the best capacity to simulate the daily precipitation of the evaluated rainfall gauge station, even for dry periods, which are known to be more difficult to be simulated in relation to the rainy periods. In this case, data entries lagged by 4 days and 5 days performed better, with Nash values close to 1.0 and root mean square errors below 0.001.
Acesso aberto (Open Access)
Estimativas de curvas IDF e curvas de permanência na Amazônia sob a influência de mudanças climáticas
(Universidade Federal do Pará, 2021-02-05) COSTA, Carlos Eduardo Aguiar de Souza; BLANCO, Claudio José Cavalcante; http://lattes.cnpq.br/8319326553139808
The impacts on global water resources may be more intense due to climate change, making access to water more difficult and, consequently, maintaining life. In the Amazon, the effect may be even worse, as it is one of the regions most vulnerable to these changes. Representative Concentration Pathways (RCPs) scenarios are essential tools for General Circulation Models (GCMs) and Global Hydrological Models (GHMs) to simulate future climate change. Intensity, Duration and Frequency (IDF) curves and flow duration curves are fundamental for the elaboration of hydraulic projects and risk management. Thus, the objective of this study was to elaborate projections of IDF curves for the Tapajós watershed in RCP 4.5 and 8.5, using data from GCMs HadGEM2-ES, CanESM2 and MIROC5. Another objective was to analyze variations in the permanence curves and available volumes of the Amazon River using data from the GHM WaterGAP2 forced by MIROC5 and HadGEM2-ES (in RCPs 6.0 and 8.5). The projected IDF curves were compared with the existing IDF, elaborated using a stationary method. The base permanence curves were created from the last 20 years of observed flows and compared with the curves of future scenarios (from 2020). They were calculated from decadal volumes. The biggest differences for the projected IDF curves were in MIROC5 (143.15% in RCP 8.5) and the smallest differences were in HadGEM2-ES (4% in RCP 4.5) both for the 100-year return period. The spatial resolutions of each GCM influenced their IDF curves, since CanESM2 did not present satisfactory results and MIROC5 was the one that best represented the possible future differences. WaterGAP2 presented the classification “Very Good” for most stations according to statistical validation indicators. Most of the extreme flows were for 2080 to 2099. For WaterGAP2 (MIROC5), most volumes were below the century's decadal average, increasing from 2060. For WaterGAP2 (HadGEM2-ES) projections the volumes are usually close or below the decadal average, falling from 2060 onwards. MIROC5 is the most suitable for studies of climate projections in the Amazon.

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