Gerenciamento ótimo das pressões em redes de abastecimento de água através da criação de distritos de medição com base na aprendizagem de máquinas

Integrated management of water supply systems with efficient use of natural resources requires optimization of operational performances. Dividing the water supply networks into small units, so-called district metered areas (DMAs), is a strategy that allows the development of specific operational rules, responsible for improving the network performance. In this context, clustering methods congregate neighboring nodes in groups according to similar features, such as elevation or distance to the water source. Taking into account hydraulic, operational and mathematical criteria to determine the configuration of DMAs, this work presents the k-means model and a hybrid model, that combines a self-organizing map (SOM) with the k-means algorithm, as clustering methods, comparing four mathematical criteria to determine the number of DMAs, namely Silhouette, GAP, Calinski-Harabasz and Davies Bouldin. The influence of three clustering topological criteria is evaluated: the water demand, node elevation and pipe length, in order to determine the optimal number of clusters. Furthermore, to identify the best DMA configuration, the particle swarm optimization (PSO) method was applied to determine the number, cost, pressure setting of Pressure Reducing Valves and location of DMA entrances.24A gestão integrada dos sistemas de abastecimento de água com o uso eficiente dos recursos requer a otimização das operações. O agrupamento das redes de abastecimento de água em pequenas unidades, chamadas de distritos de medição (DMAs), é uma estratégia que permite o desenvolvimento de regras operacionais específicas, responsáveis por melhorar o desempenho da rede. Neste contexto, os métodos de classificação agrupam os nós vizinhos de acordo com características semelhantes, como elevação ou distância à fonte de água. Utilizando os critérios topológicos, operacionais e matemáticos para determinar a configuração dos DMAs, o trabalho apresenta um modelo k-means e um modelo híbrido, que combina um mapa auto-organizado (SOM) com o algoritmo k-means, como métodos de agrupamento. Comparou-se quatro critérios matemáticos, Silhouette, GAP, CalinskiHarabasz e Davies-Bouldin e analisou-se a influência de três critérios topológicos variáveis, a demanda de água, a elevação dos nós e o comprimento do tubo, para determinar o número ótimo de agrupamentos. Ademais, com o intuito de identificar a melhor configuração de DMAs, o método de otimização de enxame de partículas (PSO) foi aplicado para determinar o número, o custo, as pressões e a localização das entradas do DMA

A spatial statistics approach to land use cover change modeling.

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Joint operation of pressure reducing valves and pumps for improving the efficiency of water distribution systems

[EN] New environmental paradigms imposed by climate change and urbanization processes are leading cities to rethink urban management services. Propelled by technological development and the internet of things, an increasingly smart management of cities has favored the emergence of a new research field, namely, the smart city. Within this new way of considering cities, smart water systems are emerging for the planning, operation, and management of water distribution networks (WDNs) with maximum efficiency derived from the application of data analysis and other information technology tools. Considering the possibility of improving WDN operation using available demand data, this work proposes a hybrid and near-real-time optimization algorithm to jointly manage pumps working with variable speed drives and pressure-reducing valves for maximum operational efficiency. A near-real-time demand forecasting model is coupled with an optimization algorithm that updates in real time the water demand of the hydraulic model and can be used to define optimal operations. The D-town WDN is used to validate the proposal. The number of control devices in this WDN makes real time control especially complex. Warm solutions are proposed to cope with this feature as they reduce the computational effort needed if suitably tuned. In addition to energy savings of around 50%, the methodology proposed in this paper enables an efficient system pressure management, leading to significant leakage reduction.Brentan, BM.; Meirelles, G.; Luvizotto, E.; Izquierdo Sebastián, J. (2018). Joint operation of pressure reducing valves and pumps for improving the efficiency of water distribution systems. Journal of Water Resources Planning and Management. 144(9):04018055-1-04018055-12. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000974S04018055-104018055-12144

Hybrid SOM+k-Means Clustering to Improve Planning, Operation and Management in Water Distribution Systems

[EN] With the advance of new technologies and emergence of the concept of the smart city, there has been a dramatic increase in available information. Water distribution systems (WDSs) in which databases can be updated every few minutes are no exception. Suitable techniques to evaluate available information and produce optimized responses are necessary for planning, operation, and management. This can help identify critical characteristics, such as leakage patterns, pipes to be replaced, and other features. This paper presents a clustering method based on self-organizing maps coupled with k-means algorithms to achieve groups that can be easily labeled and used for WDS decision-making. Three case-studies are presented, namely a classification of Brazilian cities in terms of their water utilities; district metered area creation to improve pressure control; and transient pressure signal analysis to identify burst pipes. In the three cases, this hybrid technique produces excellent results. © 2018 Elsevier Ltd. All rights reserved.This work is partially supported by Capes and CNPq, Brazilian research agencies. The use of English was revised by John Rawlins.Brentan, BM.; Meirelles, G.; Luvizotto, E.; Izquierdo Sebastián, J. (2018). Hybrid SOM+k-Means Clustering to Improve Planning, Operation and Management in Water Distribution Systems. Environmental Modelling & Software. 106:77-88. https://doi.org/10.1016/j.envsoft.2018.02.013S778810

Comportamento produtivo de forrageiras cultivadas sob sombreamento no cerrado do Amapá.

O estabelecimento de pastagens na Amazônia, baseados na retirada da vegetação nativa (Cerrado e floresta), tem causado muitos prejuízos. Os sistemas silvipastoris aumentando a eficiência de utilização dos recursos naturais, surgem como alternativa, melhorando a produção de biomassa do sistema e a renda do produtor. Para o sucesso dos sistemas silvipastoris deve-se selecionar espécies forrageiras adaptadas ao sombreamento de árvores. Uma questão que chama atenção é que os programas de melhoramento das plantas forrageiras tem sido desenvolvidos em condições de plena luz e, as espécies selecionadas podem não ser tolerantes a sombra. O presente trabalho, objetivou avaliar no cerrado do Amapá, o comportamento produtivo de sete gramíneas e seis leguminosas forrageiras, sob três regimes de luminosidade em sub-bosque de taxi-branco (Slerolobium paniculatum): Pleno sol; sombreamento moderado (417 árvores/ha) e sombreamento intenso (833 árvores/ha). As espécies avaliadas, apresentaram respostas distintas e negativas às condições de sombreamento, sendo que o sombreamento intenso (833 árvores/ha) prejudicou severamente o desempenho das plantas. A gramínea B. brizantha cv. Marandú e a leguminosa S. guianensis cv. Mineirão, apresentaram os melhores desempenhos sob condição de sombreamento moderado, destacando-se como promissoras para futuros ensaios envolvendo a avaliação de sistemas silvipastoris para o cerrado do Amapá.bitstream/item/63023/1/AP-Comportamento-produtivo-forrageira.pd

District metered area design through multicriteria and multiobjective optimization

The design of district metered areas (DMA) in potable water supply systems is of paramount importance for water utilities to properly manage their systems. Concomitant to their main objective, namely, to deliver quality water to consumers, the benefits include leakage reduction and prompt reaction in cases of natural or malicious contamination events. Given the structure of a water distribution network (WDN), graph theory is the basis for DMA design, and clustering algorithms can be applied to perform the partitioning. However, such sectorization entails a number of network modifications (installing cut-off valves and metering and control devices) involving costs and operation changes, which have to be carefully studied and optimized. Given the complexity of WDNs, optimization is usually performed using metaheuristic algorithms. In turn, optimization may be single or multiple-objective. In this last case, a large number of solutions, frequently integrating the Pareto front, may be produced. The decision maker has eventually to choose one among them, what may be tough task. Multicriteria decision methods may be applied to support this last step of the decision-making process. In this paper, DMA design is addressed by (i) proposing a modified k-means algorithm for partitioning, (ii) using a multiobjective particle swarm optimization to suitably place partitioning devices, (iii) using fuzzy analytic hierarchy process (FAHP) to weight the four objective functions considered, and (iv) using technique for order of preference by similarity to ideal solution (TOPSIS) to rank the Pareto solutions to support the decision. This joint approach is applied in a case of a well-known WDN of the literature, and the results are discussed

Erosive potential of rains in the climate change scenarios in the upper Taquari River Basin, MS, Brazil.

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