1,721,154 research outputs found
Performance of Implicit Stochastic Approaches to the Synthesis of Multireservoir Operating Rules
No full textWith increasing pressure on water resources availability and dependability and constraints due to environmental concerns, the traditional approaches for defining reservoir management rules are often inadequate. In particular, in multireservoir systems, when multiple input variables (e.g., the storage of other reservoirs in the system, water demand in different districts) must be taken into account, it is almost impossible to figure out which shape the operating rule(s) could have. For these reasons, neural network (NN) based rules have been increasingly adopted in the last decade. NN-based rules are well known as universal approximators that can help determine the most interesting input variables, their mutual relations, and how they contribute to the definition of the optimal releases. Two approaches to the identification of neural management rules are discussed in the paper. The first solves a deterministic open-loop (i.e., with known inflows) problem and then identifies neural closed-loop policies using the classical regression method, so that the rules approximate as much as possible the solution found in the first step. The second approach, direct policy search, assumes that the operating rule is represented by an NN, the parameters of which are optimized directly by solving the optimal closed-loop problem. This work applies the two approaches to the case of the downstream portion of the Nile River basin system, which contains some large reservoirs, and for which several years of synthetic streamflows are available. The comparison of the two approaches highlights intrinsic differences, showing the benefits and disadvantages of each. In the specific case of the Nile, the first approach performs better in terms of global agricultural deficit and hydropower production
Forecasting of noisy chaotic systems with deep neural networks
Full text linkRecurrent neural networks have recently proved the state-of-the-art approach in forecasting complex oscillatory time series on a multi-step horizon. Researchers in the field investigated different machine learning techniques and training approaches on dynamical systems with different degrees of complexity. Still, these analyses are usually limited to noise-free chaotic time series. This paper extends the analysis from a deterministic to a noisy environment, by considering both observation and structural noise. Observation noise is evaluated by adding different levels of artificially-generated random values on deterministic processes obtained from the simulation of four archetypal chaotic systems. A case of structural noise is implemented through a time-varying version of the logistic map, which exhibits a slow structural change of the system's dynamic that makes the system non-stationary. Finally, a time series of ozone concentration in Northern Italy is considered to test the theoretical findings on a real-world case study in which both forms of noise play a significant role. Recurrent neural networks formed by LSTM cells are compared with two benchmark feed-forward architectures. LSTM trained without the standard teacher forcing approach, i.e., with training that replicates the setting used in inference mode, proved to have the best performance in compensating the stochasticity generated by the observation noise and reproducing the structural non-stationarity of the process
Optimal air pollution control strategies: a case study
No full textAir pollution can be controlled at a regional level in several different ways, such as emission standards, taxes, permits, etc. The European Community decided to set standards on environmental quality, namely on the distribution of pollutant concentrations measured at ground level. This paper deals with the problem of evaluating the trade-offs between such ambient standards and pollution abatement costs. For this purpose, a two-objective linear program is formulated and solved for a 300 km2 region in northern Italy, using a simulation model to evaluate the effects of each pollution source. The software developed forms the basis of a more complete decision support system for this type of complex problem. Its structure and components are described in detail
Helicobacter pylori infection and parietal cell antibodies in children: is there a link?
No full textSensitivity of Chaotic Dynamics Prediction to Observation Noise
Full text linkRecent advances in nonlinear time-series prediction demonstrated the ability of recurrent neural network to forecast chaotic time series on a multi-step horizon, outperforming previous approaches. Researches considered chaotic systems with different degree of complexity, but the analysis was mainly limited to the noise-free case. In this work, we extend the analysis to a noisy environment, in order to fill the gap between deterministic and real-world time series. We consider four archetypal deterministic chaotic systems each with different levels of additive noise, representing the observation uncertainty always affecting practical applications. A time series of solar irradiance is also taken into account as a real-world case study. Various neural architectures, including feed-forward and recurrent networks, are adopted as predictors. LSTM cells are used as recurrent neurons, with a special focus on the training approach. As in the noise-free case, LSTM trained without the traditional teacher forcing, i.e., with a training that replicates the forecasting conditions, proved to be the best architecture. The experiments on the archetypal systems also shows that the error due to the model identification is negligible if compared to the one caused by a small observation noise. In other words, system identification and predictions are well distinct tasks
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