Water4Cities: An ICT platform enabling Holistic Surface Water and Groundwater Management for Sustainable Cities

To enable effective decision-making at the entire city level, both surface water and groundwater should be viewed as part of the extended urban water ecosystem with its spatiotemporal availability, quantity, quality and competing uses being taken into account. The Water4Cities project aims to build an ICT solution for the monitoring, visualization and analysis of urban water at a holistic urban setting to provide added-value decision support services to multiple water stakeholders. This paper presents the main stakeholders identified, the overall approach and the target use cases, where Water4Cities platform will be tested and validated

Daily multivariate forecasting of water demand in a touristic island with the use of artificial neural network and adaptive neuro-fuzzy inference system

Water demand forecast has emerged as an imperative component of intelligent Internet and Communication Technologies based methodologies of water management. The need of increased time resolution of forecast in order to implement such methodologies is driving stakeholders to long for new more specialized forecast approaches that will take into account the special drivers of water demand in each case study. Advanced techniques have the ability to overcome the nonlinearity issues commonly met when investigating the complex relationship of water demand and weather, socioeconomic and other variables. In this article we present two approaches, an Artificial Neural Network and an Adaptive Neuro-Fuzzy Inference System, for forecasting a Mediterranean touristic resort daily water demand based on weather variables, tourism and leakage. Both models seem to have an adequate response, though ANFIS can more smoothly catch winter non-touristic water demand profile. © 2016 IEEE

Sistema inteligente para o controle de pressão De redes de distribuição de água abastecidas Por bombas associadas em paralelo

The objective of this research is to develop an intelligent system based on artificial neural networks for water distribution systems that operate with pumps associated in parallel. The system aims at process automation and the definition of operating state for electric motors (on, off or with partial rotation), aiming at the same time the pressure control and reduction of electric power consumption. The developed intelligent system is a generic one, which allows the application of control structure in similar processes, and it was applied in a fully instrumented test rig that emulates a real system of water supply. The results showed that the performance of the artificial neural network is quite satisfactory, and thus can be successfully implemented in other similar water distribution systems in order to reduce consumption of water and electric energy, decrease costs of maintenance, and increase the degree of reliability of operational procedures.O objetivo desta pesquisa é desenvolver um sistema inteligente baseado em redes neurais artificiais para redes de distribuição de água que operam com bombas associadas em paralelo. O sistema tem por finalidade a automação do processo e a definição do estado de funcionamento dos motores elétricos (ligado, desligado ou com rotação parcial), visando simultaneamente ao controle de pressão e à redução do consumo de energia elétrica. O sistema inteligente desenvolvido é genérico, o que permite a aplicação da estrutura de controle em processos semelhantes, e foi aplicado em uma bancada experimental totalmente instrumentalizada que emula um sistema real de abastecimento de água. Os resultados mostraram que o desempenho da rede neural artificial é bastante satisfatório, e, assim, poderá ser implementada com sucesso em outros sistemas de distribuição de água similares, a fim de proporcionar redução do consumo de água e energia elétrica, diminuição dos custos de manutenção e aumento do grau de confiabilidade dos procedimentos operacionais

Probabilistic approach of reservoir level depletion induced by drought

Droughts are one of the most complicated natural disasters on earth. The repetitive occurrence of droughts has enormous adverse impacts on different aspects of human lives and natural environment. Careful monitoring and early warning systems can assist in the development of effective drought management strategies. Therefore, it is of immense significance to have a full understanding of the characteristics of a developing drought (severity, frequency etc.) before planning any drought response measures. The main aim of this research is to develop a methodology to evaluate reservoir storage levels during drought periods in a probabilistic way. In doing so, a case study was conducted of the Upper Yarra reservoir, which is located in the upper part of the Yarra River catchment in Australia. In order to identify the impacts of drought on this reservoir, it is important to have detailed knowledge of the general drought conditions surrounding this reservoir, as major portions of its inflow are harvested from neighbouring areas. Therefore, a comprehensive investigation of drought characteristics over this area is essential. Six rainfall and six streamflow stations near the Upper Yarra reservoir were selected for evaluating meteorological and hydrological drought events using the Standardized Precipitation Index (SPI) and the Standardized Hydrological Drought Index (SHDI), respectively. Both of these indices detected drought events successfully when applied to the data. Univariate and bivariate frequency analysis of drought duration and severity were carried out using the Gumbel-Hougaard copula. A probabilistic assessment of the reservoir storage condition was carried out by joint consideration of probability of initial storage volume and probability of drought events affecting inflow to the reservoir. Therefore, frequency analysis of drought events of inflow to the reservoir with particular severity and duration were conducted before applying them to the reservoir system model with specific initial water levels. The quantitative exploration of trends of drought characteristics (e.g. severity, frequency) provides meaningful insight to water authorities for developing of drought management plans. This study employed basic and modified Mann-Kendall tests to detect monotonic trends in drought characteristics. Both tests identified significant decreasing trends for four stations in the study area. More specific results of trends were reported by Innovative Trend Analysis (ITA) method. The results indicate that extreme drought situations are more likely to appear at the Reefton, Warburton, Alderman Creek, Little Yarra and McMahons Creek stations. Using the Sequential Mann-Kendall test, it was observed that the starts of the abrupt change points for most stations were found during the Millennium Drought (1996 to 2009) in Victoria. The changing patterns of drought frequencies were also investigated using the Poisson regression method. All stations exhibited decreasing trends in inter-arrival times between successive drought events, indicating that droughts are becoming more frequent in this catchment. The integrated modelling software Source is used to construct a reservoir system model. The development of water demand function is an essential requirement for building of the reservoir system model by Source software. Multiple regression analysis (MRA) and principal component analysis (PCA) are used and, finally, PCA was selected for development of water demand function because PCA gives better results than MRA. This study determines a risk assessment of storage condition of the Upper Yarra reservoir due to impacts of drought events. A probabilistic approach is proposed, taking into account the variability of reservoir storage volume prior to a drought event and different drought scenarios. Both drought severity and durations are included in developing drought scenarios. All required inputs are used in Source software to determine the reservoir storage volume at the end of a drought event. The analysis is performed for Period 3 (June to August, the most critical time of a year in terms of availability of water in the reservoir) and Period 1 (December to February, the least critical time). Three prespecified storage conditions are studied: (1) when storage drops < 50% of its full supply volume (FSV) (CC1); (2) when storage drops < 40% of FSV (CC2); and (3) when storage drops < 30% of FSV (CC3). The main conclusions of these analyses are summarized as follows: 1) the probability of storage reduction below the prespecified conditions is higher in Period 3 than in Period 1; 2) the risk of storage reduction can be successfully evaluated based on two uncertain parameters (initial storage volume and drought severity) and the results show that the initial storage volume is a more dominant uncertain parameter in probability calculation than drought severity for long as well as short-duration droughts; 3) several drought zones are successfully constructed for each condition on plots of initial storage vs. drought severity. It should be noted that each zone is constructed for a specific drought duration and period. If needed, other zones can be developed for other periods and drought durations following the same approach; 4) the constructed zones will give indications to water authorities about the reduction of storage due to long- and short-duration drought events; 5) finally, the general form of the relationship between initial storage volume and probability of storage reduction below any particular level for any drought event of known duration and severity is developed. Results of this study provide a technical reference for the risk assessment of reservoirs due to drought events and will assist in the development of appropriate action plans