Real-time daily flow forecasting using black-box models, diffusion processes, and neural networks

2000 ◽  
Vol 27 (4) ◽  
pp. 671-682 ◽  
Author(s):  
N Lauzon ◽  
J Rousselle ◽  
S Birikundavyi ◽  
H T Trung
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Diffusion Process ◽  
Diffusion Processes ◽  
Black Box ◽  
Linear Type ◽  
Modeling Approaches ◽  
Box Models ◽  
Almost All ◽  
Black Box Models

The purpose of this study is to compare three modeling approaches used for the prediction of daily natural flows 1-7 days ahead. Linear black-box models, which have been commonly used for modeling flows, constitute the first approach. The second approach, a linear type in the context of our application, is less known in the water resources field and is identified by the term diffusion process. The third approach uses models called neural networks, which have gained interest in many fields. All these approaches were tested on 15 watersheds from the Saguenay - Lac-Saint-Jean hydrographic system, located in the province of Quebec, Canada. Because the watersheds possess different physical characteristics, the models were tested under several runoff conditions. In this article, the focus is on results; all approaches along with their conditions of use have been detailed elsewhere in the literature. The results obtained showed that neural networks constitute, for almost all the watersheds studied, the best approach to forecast daily natural flows. The more flexible structure of neural networks allows a best reproduction of complex runoff conditions. However, neural networks are more sensitive to outliers present in observed natural flow series, which are used as inputs in the three models tested. In practice, to model flows at specific periods of the year, it seems preferable to establish seasonal models. If a neural network has an inadequate structure for the period under consideration, then it may produce less convincing results than the other two modeling approaches tested in this study.Key words: forecasts, flows, black-box model, diffusion process, neural network.

scholarly journals Modelling Acetification with Artificial Neural Networks and Comparison with Alternative Procedures

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 749 ◽  
Author(s):  
Jorge E. Jiménez-Hornero ◽  
Inés María Santos-Dueñas ◽  
Isidoro García-García
Keyword(s):  
Neural Networks ◽  
Acetic Acid ◽  
Artificial Neural Networks ◽  
First Principles ◽  
Goodness Of Fit ◽  
Black Box ◽  
Operating Conditions ◽  
Box Models ◽  
Artificial Neural ◽  
Black Box Models

Modelling techniques allow certain processes to be characterized and optimized without the need for experimentation. One of the crucial steps in vinegar production is the biotransformation of ethanol into acetic acid by acetic bacteria. This step has been extensively studied by using two predictive models: first-principles models and black-box models. The fact that first-principles models are less accurate than black-box models under extreme bacterial growth conditions suggests that the kinetic equations used by the former, and hence their goodness of fit, can be further improved. By contrast, black-box models predict acetic acid production accurately enough under virtually any operating conditions. In this work, we trained black-box models based on Artificial Neural Networks (ANNs) of the multilayer perceptron (MLP) type and containing a single hidden layer to model acetification. The small number of data typically available for a bioprocess makes it rather difficult to identify the most suitable type of ANN architecture in terms of indices such as the mean square error (MSE). This places ANN methodology at a disadvantage against alternative techniques and, especially, polynomial modelling.

scholarly journals Hybrid Machine Learning Approaches and a Systematic Model Selection Process for Predicting Soot Emissions in Compression Ignition Engines

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7865
Author(s):  
Saeid Shahpouri ◽  
Armin Norouzi ◽  
Christopher Hayduk ◽  
Reza Rezaei ◽  
Mahdi Shahbakhti ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Selection ◽  
Black Box ◽  
Box Model ◽  
Feature Sets ◽  
Machine Learning Methods ◽  
Box Models ◽  
Soot Emissions ◽  
Black Box Models

The standards for emissions from diesel engines are becoming more stringent and accurate emission modeling is crucial in order to control the engine to meet these standards. Soot emissions are formed through a complex process and are challenging to model. A comprehensive analysis of diesel engine soot emissions modeling for control applications is presented in this paper. Physical, black-box, and gray-box models are developed for soot emissions prediction. Additionally, different feature sets based on the least absolute shrinkage and selection operator (LASSO) feature selection method and physical knowledge are examined to develop computationally efficient soot models with good precision. The physical model is a virtual engine modeled in GT-Power software that is parameterized using a portion of experimental data. Different machine learning methods, including Regression Tree (RT), Ensemble of Regression Trees (ERT), Support Vector Machines (SVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Bayesian Neural Network (BNN) are used to develop the black-box models. The gray-box models include a combination of the physical and black-box models. A total of five feature sets and eight different machine learning methods are tested. An analysis of the accuracy, training time and test time of the models is performed using the K-means clustering algorithm. It provides a systematic way for categorizing the feature sets and methods based on their performance and selecting the best method for a specific application. According to the analysis, the black-box model consisting of GPR and feature selection by LASSO shows the best performance with test R2 of 0.96. The best gray-box model consists of SVM-based method with physical insight feature set along with LASSO for feature selection with test R2 of 0.97.

scholarly journals Implementation of Black Box Models for Internal Ballistics Optimization Using an Artificial Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jie Chen ◽  
JingYin Li ◽  
ShuangXi Li ◽  
YunXiang You
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rotational Speed ◽  
Black Box ◽  
Optimization Techniques ◽  
Low Energy ◽  
Internal Ballistics ◽  
Box Models ◽  
Artificial Neural ◽  
Black Box Models

The process of UUV delivery is a typical nonlinear transient dynamic phenomenon, which is generally described by the internal ballistic model. Evaluation of optimal internal ballistics parameters is a key step for promoting ballistic weapon performance under given launch constraints. Hence, accurate and efficient optimization techniques are required in ballistics technology. In this study, an artificial neural network (ANN) is used to simplify the process of regression analysis. To this end, an internal ballistics model is built in this study as a black box for a classic underwater launching system, such as a torpedo launcher, based on ANN parameter identification. The established black box models are mainly employed to calculate the velocity of a ballistic body and the torque of a launching pump. Typical internal ballistics test data are adopted as samples for training the ANN. Comparative results demonstrate that the developed black box models can accurately reflect changes in internal ballistics parameters according to rotational speed variations. Therefore, the proposed approach can be fruitfully applied to the task of internal ballistics optimization. The optimization of internal ballistics precision control, optimal control of the launching pump, and optimal low-energy launch control were, respectively, realized in conjunction with the established model using the SHERPA search algorithm. The results demonstrate that the optimized internal ballistics rotational speed curve can achieve the optimization objectives of low-energy launch and peak power while meeting the requirements of optimization constraints.

scholarly journals Reconstruction of pairwise interactions using energy-based models*

2021 ◽  
Vol 2021 (12) ◽  
pp. 124007
Author(s):  
Christoph Feinauer ◽  
Carlo Lucibello
Keyword(s):  
Neural Network ◽  
Ising Model ◽  
Black Box ◽  
General Idea ◽  
Pairwise Interactions ◽  
Box Models ◽  
Pairwise Models ◽  
Pairwise Model ◽  
Interpretable Models ◽  
Black Box Models

Abstract Pairwise models like the Ising model or the generalized Potts model have found many successful applications in fields like physics, biology, and economics. Closely connected is the problem of inverse statistical mechanics, where the goal is to infer the parameters of such models given observed data. An open problem in this field is the question of how to train these models in the case where the data contain additional higher-order interactions that are not present in the pairwise model. In this work, we propose an approach based on energy-based models and pseudolikelihood maximization to address these complications: we show that hybrid models, which combine a pairwise model and a neural network, can lead to significant improvements in the reconstruction of pairwise interactions. We show these improvements to hold consistently when compared to a standard approach using only the pairwise model and to an approach using only a neural network. This is in line with the general idea that simple interpretable models and complex black-box models are not necessarily a dichotomy: interpolating these two classes of models can allow to keep some advantages of both.

scholarly journals Branch and Bound Algorithm Based on Prediction Error of Metamodel for Computational Electromagnetics

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6749
Author(s):  
Reda El Bechari ◽  
Stéphane Brisset ◽  
Stéphane Clénet ◽  
Frédéric Guyomarch ◽  
Jean Claude Mipo
Keyword(s):  
Branch And Bound ◽  
Prediction Error ◽  
Global Solutions ◽  
Black Box ◽  
High Fidelity ◽  
Electromagnetic Devices ◽  
Box Models ◽  
Element Simulation ◽  
The Cost ◽  
Black Box Models

Metamodels proved to be a very efficient strategy for optimizing expensive black-box models, e.g., Finite Element simulation for electromagnetic devices. It enables the reduction of the computational burden for optimization purposes. However, the conventional approach of using metamodels presents limitations such as the cost of metamodel fitting and infill criteria problem-solving. This paper proposes a new algorithm that combines metamodels with a branch and bound (B&B) strategy. However, the efficiency of the B&B algorithm relies on the estimation of the bounds; therefore, we investigated the prediction error given by metamodels to predict the bounds. This combination leads to high fidelity global solutions. We propose a comparison protocol to assess the approach’s performances with respect to those of other algorithms of different categories. Then, two electromagnetic optimization benchmarks are treated. This paper gives practical insights into algorithms that can be used when optimizing electromagnetic devices.

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