Appliance for Water consumption management in showering by using Arduino kit, Energy NExus and SensorS : AWARENESS kit

Peer reviewe

Machine learning models for stream-level predictions using readings from satellite and ground gauging stations

While the accuracy of flood predictions is likely to improve with increasing gauging station networks and robust radar coverage, challenges arise when such sources are spatially limited [1]. For instance, severe rainfall events in the UK come mostly from the North Atlantic area where gauges are ineffective and radar instruments are limited to it 250km range. In these cases, NASA’s IMERG is an alternative source of precipitation estimates offering global coverage with 0.1-degree spatial resolution at 30-minute intervals. The IMERG estimates for the UK’s case can offer an opportunity to extend the zone of rainfall detection beyond the radar range and increase lead time on flood risk predictions [2]. This study investigates the ability of machine learning (ML) models to capture the patterns between rainfall and stream level, observed during 20 years in the River Crane in the UK. To compare performances, the models use two sources of rainfall data as input for stream level prediction, the IMERG final run estimates and rain gauge readings. Among the three IMERG products (early, late, and final), the final run was selected for this study due to its higher accuracy in rainfall estimates. The rainfall data was retrieved from rain gauges and the pixel in the IMERG dataset grid closest to the point where stream level readings were taken. These datasets were assessed regarding their correlation with stream level using cross-correlation analysis. The assessment revealed a small variance in the lags and correlation coefficients between the stream-level and the IMERG dataset compared to the lags and coefficients found between stream-level and the gauge’s datasets. To evaluate and compare the performance of each dataset as input in ML models for stream-level predictions, three models were selected: NARX, LSTM, and GRU. Both inputs performed well in the NARX model and produced stream-level predictions of high precision with MSE equal to 1.5×10-5 while using gauge data and 1.9×10-5 for the IMERG data. The LSTM model also produced good predictions, however, the MSE was considerably higher, MSE of 1.8×10-3 for gauging data and 4.9×10-3 for IMERG data. Similar performance was observed in the GRU predictions with MSE of 1.9×10-3 for gauging data and 5.6×10-3 for IMERG. Nonetheless, the results of all models are within acceptable ranges of efficacy confirming the applicability of ML models on stream-level prediction based just on rainfall and stream-level information. More importantly, the small difference between the results obtained from IMERG estimates and gauging data seems promising for future tests of IMERG rainfall data sourced from other pixels of the dataset’s grid and to explore the potential for increased lead time of predictions.Peer reviewe

Optimising oceanic rainfall estimates for increased lead time of stream level forecasting: A case study of GPM IMERG estimates application in the UK

Among the three main rainfall data sources (rain gauge stations, rainfall radar stations and weather satellites), satellites are often the most appropriate for longer lead times in real-time flood forecasting [1]. This is particularly relevant in the UK, where severe rainfall events often originate over the Atlantic Ocean, distant from land-based instruments although it can also limit the effectiveness of satellite data for long-term predictions [2]. The Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) estimates can be used as an alternative source for rainfall information in real-time flood forecasting models. However, the challenge lies in monitoring the vast oceanic region around the UK and integrating this extensive data into hydrological or data-driven models, which presents computational and time constraints. Identifying key monitoring area for obtaining these estimates is essential to address these challenges and to effectively use this use for water level forecasting in urban drainage systems (UDS). This study introduced an optimised data-driven model for streamline the collection and use of GPM IMERG rainfall estimates for water level forecasting in UDS. The model’s effectiveness was demonstrated using a 20-year satellite data set from the Atlantic Ocean, west of the UK, focusing on water level forecasting for a specific UDS point in London. This data helped identify the most probable path of rainfall from the Atlantic that impacts UDS water levels. We conducted a cross-correlation analysis between the water level records and each IMERG data pixel within the selected oceanic area. The analysis successfully pinpointed the most influential rainfall points/pixels along the Atlantic path and their respective lag times between rainfall occurrence and water level changes at any satellite-monitored point until it reaches the mainland and joins the river system. This research enhances understanding of long-distance rainfall patterns while optimising the use of GPM IMERG data. It also aids in reducing data volume and processing time for stream-level forecasting models, aiming for longer lead times.Peer reviewe

A critical review of digital technology innovations for early warning of water-related disease outbreaks associated with climatic hazards

Water-related climatic disasters pose a significant threat to human health due to the potential of disease outbreaks, which are exacerbated by climate change. Therefore, it is crucial to predict their occurrence with sufficient lead time to allow for contingency plans to reduce risks to the population. Opportunities to address this challenge can be found in the rapid evolution of digital technologies. This study conducted a critical analysis of recent publications investigating advanced technologies and digital innovations for forecasting, alerting, and responding to water-related extreme events, particularly flooding, which is often linked to disaster-related disease outbreaks. The results indicate that certain digital innovations, such as portable and local sensors integrated with web-based platforms are new era for predicting events, developing control strategies and establishing early warning systems. Other technologies, such as augmented reality, virtual reality, and social media, can be more effective for monitoring flood spread, disseminating before/during the event information, and issuing warnings or directing emergency responses. The study also identified that the collection and translation of reliable data into information can be a major challenge for effective early warning systems and the adoption of digital innovations in disaster management. Augmented reality, and digital twin technologies should be further explored as valuable tools for better providing of communicating complex information on disaster development and response strategies to a wider range of audiences, particularly non-experts. This can help to increase community engagement in designing and operating effective early warning systems that can reduce the health impact of climatic disasters

Impact of water and sanitation services on cholera outbreaks in sub-Saharan Africa

While most parts of the world seem to have controlled cholera, the sub-Saharan African region is still suffering with the cholera outbreaks and struggling to restrain its incidence. Recent research attributes eighty three percent of cholera deaths between 2000 and 2015 to the sub-Saharan region. Poor water, sanitation and hygiene (WASH) services can be among the main risk factors contributing to the public health burden of cholera. Humans living in close proximity to one another in environments with poor hygiene conditions and little access to clean water is an explanation for how cholera takes root in non-coastal areas. The combination of these factors with the vulnerability of surface and groundwater resources to faecal contamination can favour onset and propagation of outbreaks. This study investigated the correlation between cholera rates per population and lack of basic services of drinking water and sanitation in the sub-Saharan African countries, where incident cases of cholera have been regularly reported to the World Health Organization (WHO) since 1991

Machine learning models for stream-level predictions using readings from satellite and ground gauging stations

While the accuracy of flood predictions is likely to improve with increasing gauging station networks and robust radar coverage, challenges arise when such sources are spatially limited [1]. For instance, severe rainfall events in the UK come mostly from the North Atlantic area where gauges are ineffective and radar instruments are limited to it 250km range. In these cases, NASA’s IMERG is an alternative source of precipitation estimates offering global coverage with 0.1-degree spatial resolution at 30-minute intervals. The IMERG estimates for the UK’s case can offer an opportunity to extend the zone of rainfall detection beyond the radar range and increase lead time on flood risk predictions [2]. This study investigates the ability of machine learning (ML) models to capture the patterns between rainfall and stream level, observed during 20 years in the River Crane in the UK. To compare performances, the models use two sources of rainfall data as input for stream level prediction, the IMERG final run estimates and rain gauge readings. Among the three IMERG products (early, late, and final), the final run was selected for this study due to its higher accuracy in rainfall estimates. The rainfall data was retrieved from rain gauges and the pixel in the IMERG dataset grid closest to the point where stream level readings were taken. These datasets were assessed regarding their correlation with stream level using cross-correlation analysis. The assessment revealed a small variance in the lags and correlation coefficients between the stream-level and the IMERG dataset compared to the lags and coefficients found between stream-level and the gauge’s datasets. To evaluate and compare the performance of each dataset as input in ML models for stream-level predictions, three models were selected:NARX, LSTM, and GRU. Both inputs performed well in the NARX model and produced stream-level predictions of high precision with MSE equal to 1.5×10-5 while using gauge data and 1.9×10-5 for the IMERG data. The LSTM model also produced good predictions, however, the MSE was considerably higher, MSE of 1.8×10-3 for gauging data and 4.9×10-3 for IMERG data. Similar performance was observed in the GRU predictions with MSE of 1.9×10-3 for gauging data and 5.6×10-3 for IMERG. Nonetheless, the results of all models are within acceptable ranges of efficacy confirming the applicability of ML models on stream-level prediction based just on rainfall and stream-level information. More importantly, the small difference between the results obtained from IMERG estimates and gauging data seems promising for future tests of IMERG rainfall data sourced from other pixels of the dataset’s grid and to explore the potential for increased lead time of predictions

Experimentação de um dispositivo-corpo em uma vivência drag: pesquisar pelo afetar

Esse artigo parte de um trabalho de conclusão de curso de Psicologia, que consistiu em um pesquisar cartográfico, cujo objetivo fora mapear o cambalear existencial do pesquisador nos entre identidades de gênero/sexualidade em uma vivência de um curso de Drag Queen. O pesquisador, esquadrinhado por corpos e identidades – acadêmico e timidez-macho – se depara com a experiência performática de uma drag queen, a qual expressa a relação de gênero borrada, usando a ambiguidade como arma às hierarquias de existências protocolares. O ―caminho metodológico‖ se deu pelo próprio corpo do pesquisador, que percebeu percursos de nomadismos no curso e criou narrativas através e por este dispositivo-corpo político, estético e ético. No decorrer dessa experimentação intensiva, foi possível perceber os limites identitários do pesquisador e da própria maneira de pesquisar e escrever; perceber a finitude possibilitou inventar e arquitetar empatias afetivas por corpos-teorias-outras e suas respectivas tramas singulares de territórios habitáveis

Appliance for Water consumption management in showering by using Arduino kit, Energy NExus and SensorS Presentation : AWARENESS kit

Peer reviewe

1B107 Educação Alimentar e Nutricional: Uma estratégia de Promoção da Saúde articulada ao Ensino de Ciências

O estudo teve por objetivo propor e analisar um processo de interação entre nutricionista, professor e aluno ao desenvolver Educação Alimentar e Nutricional - EAN articulada ao ensino de ciências. A pesquisa é qualitativa, com delineamento descritivo e transversal, com participação de uma nutricionista, 26 estudantes e uma professora da disciplina de Ciências do Ensino Fundamental de uma escola pública. Os argumentos de Moraes e Galiazzi (2007) sobre análise textual discursiva fundamentaram a análise dos dados. Os resultados mostram que a interação entre os sujeitos da pesquisa contribuiu para reflexão dos hábitos alimentares dos estudantes e para significação dos conceitos de ciências. A construção da pirâmide alimentar pelos estudantes, se constituiu em atividade de reflexão e empoderamento para melhor tomada de decisão sobre sua saúde e qualidade de vida

Formação inicial de professores: uma perspectiva integradora dos conteúdos disciplinares e educação para a saúde

This paper discusses a proposal for a curricular reorganization, called Study Situation (SS), focusing on teacher training and health education. The study aims to investigate the learning that was produced by teachers in initial formation during the development of the SS “Food: Production and Consumption”, and how they mean the concepts of natural sciences from the perspective of Health Education. The SS was developed in the Training Classes of Science Teaching: Elementary School I (Biological Sciences Course) and the empirical data were produced from class discussions and analysis of 14 textual productions, taken from graduates’ journals. The results show that there are still some conceptual misunderstandings in the journals writings, but the reflection on their records made it possible to construct important learning for both teacher education and the integration of science and health education contents. In general, the graduates showed interest in new ways of teaching and learning and they produced more complex meanings about human nutrition through the understanding of chemical, physical and biological concepts articulated with the subject under study. It was verified that the initial training of teachers in this perspective contributed to the constitution of more critical and sensitive teachers to the daily problems of their students.Discute-se neste artigo uma proposta de reorganização curricular, denominada Situação de Estudo (SE), com foco na formação docente e na educação para saúde. O objetivo do estudo foi investigar que aprendizagens foram produzidas, por docentes em formação inicial, no decorrer do desenvolvimento da SE “Alimentos: Produção e Consumo” e como eles/elas significam os conceitos de ciências da natureza na perspectiva da Educação para a Saúde. A SE foi desenvolvida nas aulas de Estágio em Ensino de Ciências: Ensino Fundamental I (Curso de Ciências Biológicas), e os dados empíricos foram produzidos a partir de discussões das aulas e análise de 14 produções textuais, retiradas de diários de bordo dos/as licenciandos/as. Os resultados apontam que ainda existem alguns equívocos conceituais nas escritas em diário de bordo, mas a reflexão sobre seus registros possibilitou a construção de aprendizagens importantes tanto para formação docente quanto para integração dos conteúdos de ciências e educação para a Saúde. Os/as licenciados/as demonstraram, de forma geral, interesse por novos modos de ensinar e aprender e produziram significados mais complexos sobre a alimentação humana pelo entendimento de conceitos químicos, físicos e biológicos articulados com a temática em estudo. Verificou-se que a formação inicial de professores nesta perspectiva contribuiu para constituição de docentes mais críticos e sensíveis aos problemas cotidianos de seus alunos