Predicting Inflow Flow in Hydraulic Dams Using Artificial Neural Networks

Cursos e Congresos, C-155[Abstract] Accurate prediction of inflow in dams plays a crucial role in water resource management Kim et al. (2019); Vargas-Garay et al. (2018); Zhong et al. (2018) and risk mitigation Costabile et al. (2020); Rabuñal et al. (2007). This study focuses on the Portodemouros dam (located between the provinces of A Coruña and Pontevedra), where a model based on a Long Short-Term Memory (LSTM) artificial neural network has been implemented to predict dam inflow. The results demonstrate the well-established effectiveness of the LSTM network in flow prediction Dongkyuna and Seokkoob (2021); Jo and Jung (2023); Li et al. (2020) applied to the Portodemouros dam compared to other models. This comparison has already been performed in other studies with both mathematical models Amirreza et al. (2022); Ansori and Anwar (2022); A.R1 et al. (2018); Beck et al. (2017); Ciabatta et al. (2016); Costabile et al. (2020); Fan et al. (2013); Hermanovsky et al. (2017); Kim et al. (2019); Vargas-Garay et al. (2018); Zhong et al. (2018), genetic programming Aytek et al. (2008); Havl´ıˇcek et al. (2013); Heˇrmanovsk´y et al. (2017); Rabuñal et al. (2007) and other machine learning algorithms Jo and Jung (2023). Combining precipitation data from multiple regions and meteorological forecasts significantly enhances the model’s ability to anticipate variations in dam inflow. This improved accuracy is essential for early flood detection and informed decision-making in dam operation. This study forms part of the Marine Science programme (ThinkInAzul) supported by Ministerio de Ciencia e Innovación and Xunta de Galicia with funding from European Union NextGenerationEU (PRTR-C17.I1) and European Maritime and Fisheries Fun

Research protocol for the mortality atlas of the provincial capitals of Andalusia and Catalonia (AMCAC Project)

Comparative Study; English Abstract; Journal Article; Multicenter Study;The aim of this work is to make known the multicentric project AMCAC, whose objective is to describe the geographical distribution of mortality from all causes in census groups of the provincial capitals of Andalusia and Catalonia during 1992-2002 and 1994-2000 respectively, and to study the relationship between the sociodemographic characteristics of the census groups and mortality. This is an ecological study in which the analytical unit is the census group. The data correspond to 298,731 individuals (152,913 men and 145,818 women) who died during the study periods in the towns of Almeria, Barcelona, Cadiz, Cordoba, Girona, Granada, Huelva, Jaen, Lleida, Malaga, Seville and Tarragona during the study periods. The dependent variable is the number of deaths observed per census group. The independent variables are the percentage of unemployment, illiteracy and manual workers. Estimation of the moderated relative risk and the study of the associations among the sociodemographic characteristics of the census groups and the mortality will be done for each town and each sex using the Besag-York-Mollie model. Dissemination of the results will help to improve and broaden knowledge about the population's health, and will provide an important starting point to establish the influence of contextual variables on the health of urban populations.Investigación financiada por el Fondo de Investigación Sanitaria (Expedientes 02/1308 y 02/0735), la Consejería de Salud de la Junta de Andalucía (Expediente 128/02) y la Red de Centros de Investigación en Epidemiología y Salud Pública (RCESP).YesEl propósito de este trabajo es dar a conocer el proyecto multicéntrico AMCAC, que tiene como objetivos describir la distribución geográfica de la mortalidad por todas las causas en las secciones censales de las capitales de provincia de Andalucía y Cataluña durante el periodo 1992-2002 y 1994-2000 respectivamente, así como estudiar la relación entre las características sociodemográficas de las secciones censales y la mortalidad. Es un estudio ecológico donde la unidad de análisis es la sección censal. Se analizarán los datos relativos a 298.731 individuos (152.913 hombres y 145.818 mujeres) fallecidos en las ciudades de Almería, Barcelona, Cádiz, Córdoba, Girona, Granada, Huelva, Jaén, Lleida, Málaga, Sevilla y Tarragona durante los periodos de estudio. La variable dependiente es el número de muertes observadas por sección censal. Las variables independientes son el porcentaje de desempleo, de analfabetismo y de trabajadores manuales. La estimación del riesgo relativo suavizado y el estudio de la relación entre las características sociodemográficas de las secciones censales y la mortalidad se realizará para cada ciudad y sexo mediante el modelo Besag-York-Mollié. La difusión de los resultados ayudará a mejorar y ampliar los conocimientos sobre la salud de la población, siendo un punto de partida importante para conocer la influencia de variables contextuales en la salud de la población urbana