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Abstract:Many factors influence water quality within a reservoir. Deforestation, excessive erosion, introduction of new species, domestic and industrial waste disposal and agricultural runoff are only a few examples.It is well known by specialists in water resources management that water levels in a reservoir may also affect its quality. But how these processes occur and how appropriate water levels can be maintained are very hard questions to answer. This is because of the physical and biological processes occurring inside the water body, and also due to the various demands from society concerning water uses.Nowadays, through the use of models, knowledge of some of the conditions can enable us to predict future conditions. In many cases, reservoir models, such as physical models for water quality, may predict the future water quality situation. These models have been used successfully to enhance knowledge about the interactions among the different parts inherent to the water systems.Through the combination of water quality and optimization models, this study proposes a suitable methodology for the assessment of planning operations of a storage reservoir. The purpose of this paper is to consider a multipurpose reservoir, under different water demands and uses from societies, concerning reservoir water quality.The proposed optimization is realized through the use of dynamic programming combined with stochastic techniques that can handle the probabilistic characteristics of inflow quantity and quality. For the water quality assessment, the UNEP/ILEC one-dimensional model with two layers called PAMOLARE is applied. Finally, sensitivity analysis is carried out using a genetic algorithm model.

Abstract:Artificial neural networks (ANNs) have been applied successfully in various fields. However, ANN models depend on large sets of historical data, and are of limited use when only vague and uncertain information is available, which leads to difficulties in defining the model architecture and a low reliability of results. A conceptual fuzzy neural network (CFNN) is proposed and applied in a water quality model to simulate the Barra Bonita reservoir system, located in the southeast region of Brazil. The CFNN model consists of a rationally-defined architecture based on accumulated expert knowledge about variables and processes included in the model. A genetic algorithm is used as the training method for finding the parameters of fuzzy inference and the connection weights. The proposed model may handle the uncertainties related to the system itself, model parameterization, complexity of concepts involved and scarcity and inaccuracy of data. The CFNN showed greater robustness and reliability when dealing with systems for which data are considered to be vague, uncertain or incomplete. The CFNN model structure is easier to understand and to define than other ANN-based models. Moreover, it can help to understand the basic behaviour of the system as a whole, being a successful example of cooperation between human and machine.

The present work focus was developing a system for early automatic detection of smoke plumes in visible-light images. The system used a realistic dataset gathered in 274 different days from a total of nine real surveillance cameras, with most smoke plumes being viewed from afar and 85% of them occupying less than 5% of the image area. We employed the innovative strategy of using the whole image for classification but "asking" the neural networks to indicate, in a multidimensional output, which image regions contained a smoke plume. The multidimensional output helped to focus the detector on the smoke regions. At the same time, the use of the whole image prevented wrong image classification caused by a constrained view of the landscape under analysis. Another strategy used was to rectify the detection results using a visual explanation algorithm, Gradient-weighted Class Activation Mapping (Grad-CAM), to ensure that detections corresponded to the smoke regions in an image. The detection algorithms tested were residual neural networks (ResNet) and EfficientNet of various sizes because these two types have given good results in the past in multiple domains. The training was done using transfer learning. Our dataset contained a total of 14125 and 21203 images with and without smoke, respectively, making it, to the best of the author's knowledge, one of the largest or even the largest reported dataset in the scientific literature in terms of the number of images with smoke collected from large distances of various kilometers. This dataset was fundamental to achieve realistic results concerning smoke detection efficiency. Our best result in the test set was an Area Under Receiver Operating Characteristic curve (AUROC) of 0.949 obtained with an EfficientNet-B0.

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The reduction of water resources due to climate change and the increasing demand associated with population growth is a renewed concern. Water distribution monitoring and smart metering are essential tools to improve distribution efficiency. This paper reports on the study, design, and implementation of a smart water meter (SWM) prototype, designed for mechanical water meters that need to undergo a retrofitting process to enable automatic metering readings. Metering data is transmitted through innovative narrowband internet of things (NB-IoT) technology with low power, long-range, and effective penetration. A flexible power management design allows the introduction of an energy harvester that recovers energy from the surrounding environment and charges the internal battery. The energy harvesting feasibility was demonstrated with two proof-of-concept configurations, light and water-turbine based. The details on the performance of the proposed solution are presented, including the output voltages and harvested power. Although the energy harvesting technologies have not been integrated yet in commercial SWM applications, the results show that the integration is feasible and, once employed in a controlled environment, it can create business advantages by reducing the size and capacity of the internal batteries, enabling one to reduce the operation cost and mitigate long-term ecological problems associated with the use and disposal of batteries.