scholarly journals The extrapolation of artificial neural networks for the modelling of rainfall—runoff relationships

2005 ◽  
Vol 7 (4) ◽  
pp. 291-296 ◽  
Author(s):  
P. Hettiarachchi ◽  
M. J. Hall ◽  
A. W. Minns
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Domain Knowledge ◽  
Low Frequency ◽  
Training Data ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Data Set ◽  
Feed Forward ◽  
Artificial Neural

The last decade has seen increasing interest in the application of Artificial Neural Networks (ANNs) for the modelling of the relationship between rainfall and streamflow. Since multi-layer, feed-forward ANNs have the property of being universal approximators, they are able to capture the essence of most input–output relationships, provided that an underlying deterministic relationship exists. Unfortunately, owing to the standardisation of inputs and outputs that is required to run ANNs, a problem arises in extrapolation: if the training data set does not contain the maximum possible output value, an unmodified network will be unable to synthesise this peak value. The occurrence of high magnitude, low frequency events within short periods of record is largely fortuitous. Therefore, the confidence in the neural network model can be greatly enhanced if some methodology can be found for incorporating domain knowledge about such events into the calibration and verification procedure in addition to the available measured data sets. One possible form of additional domain knowledge is the Estimated Maximum Flood (EMF), a notional event with a small but non-negligible probability of exceedence. This study investigates the suitability of including an EMF estimate in the training set of a rainfall–runoff ANN in order to improve the extrapolation characteristics of the network. A study has been carried out in which EMFs have been included, along with recorded flood events, in the training of ANN models for six catchments in the south west of England. The results demonstrate that, with prior transformation of the runoff data to logarithms of flows, the inclusion of domain knowledge in the form of such extreme synthetic events improves the generalisation capabilities of the ANN model and does not disrupt the training process. Where guidelines are available for EMF estimation, the application of this approach is recommended as an alternative means of overcoming the inherent extrapolation problems of multi-layer, feed-forward ANNs.

scholarly journals Opportunities and Constraints in Applying Artificial Neural Networks (ANNs) in Food Authentication. Honey—A Case Study

2021 ◽  
Vol 11 (15) ◽  
pp. 6723
Author(s):  
Ariana Raluca Hategan ◽  
Romulus Puscas ◽  
Gabriela Cristea ◽  
Adriana Dehelean ◽  
Francois Guyon ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Training Sample ◽  
Training Data ◽  
Elemental Content ◽  
Neural Structure ◽  
Food Authentication ◽  
Data Set ◽  
Artificial Neural

The present work aims to test the potential of the application of Artificial Neural Networks (ANNs) for food authentication. For this purpose, honey was chosen as the working matrix. The samples were originated from two countries: Romania (50) and France (53), having as floral origins: acacia, linden, honeydew, colza, galium verum, coriander, sunflower, thyme, raspberry, lavender and chestnut. The ANNs were built on the isotope and elemental content of the investigated honey samples. This approach conducted to the development of a prediction model for geographical recognition with an accuracy of 96%. Alongside this work, distinct models were developed and tested, with the aim of identifying the most suitable configurations for this application. In this regard, improvements have been continuously performed; the most important of them consisted in overcoming the unwanted phenomenon of over-fitting, observed for the training data set. This was achieved by identifying appropriate values for the number of iterations over the training data and for the size and number of the hidden layers and by introducing of a dropout layer in the configuration of the neural structure. As a conclusion, ANNs can be successfully applied in food authenticity control, but with a degree of caution with respect to the “over optimization” of the correct classification percentage for the training sample set, which can lead to an over-fitted model.

scholarly journals Artificial neural networks predictive models. A case study: carbon and bromine concentrations prediction based on chlorination time

2013 ◽  
Vol 14 (1) ◽  
pp. 10-17
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Potable Water ◽  
Back Propagation ◽  
Ann Model ◽  
Data Set ◽  
Test Set ◽  
Feed Forward Back Propagation ◽  
Artificial Neural ◽  
Comparison Of The Results

Artificial neural networks (ANNs) are being used increasingly to predict water variables. This study offers an alternative approach to quantify the relationship between time of chlorination in potable water (due to convectional treatment procedure) and chlorination by-products concentration (expressed as carbon and bromine) with an ANN model, i.e., capturing non-linear relationships among the water quality variables. Thus, carbon and bromine concentrations in potable water (the second chosen due to the toxicity of brominated trihalomethanes, THMs) were predicted using artificial neural networks (ANNs) based mainly on multi-layer perceptrons (MLPs) architecture. The chlorination (detention) time as much as 58 hours in Athens distributed network, comprised the input variables to the ANNs models. Moreover, to develop an ANN model for estimating carbon and bromine, the available data set was partitioned into training, validation and test set. In order to reach an optimum amount of hidden layers or nodes, different architectures were tested. The quality of the ANN simulations was evaluated in terms of the error in the validation sample set for the proper interpretation of the results. The calculated sum-squared errors for training, validation and test set were 0.056, 0.039 and 0.060 respectively for the best model selected. Comparison of the results showed that a two-layer feed-forward back propagation ANN model could be used as an acceptable model for predicting carbon and bromine contained in potable water THMs.

Artificial neural networks in diagnosis of thyroid function from in vitro laboratory tests

1993 ◽  
Vol 39 (11) ◽  
pp. 2248-2253 ◽  
Author(s):  
P K Sharpe ◽  
H E Solberg ◽  
K Rootwelt ◽  
M Yearworth
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Thyroid Function ◽  
Laboratory Tests ◽  
Extreme Values ◽  
Real Life ◽  
Training Data ◽  
Data Set ◽  
Artificial Neural

Abstract We studied the potential benefit of using artificial neural networks (ANNs) for the diagnosis of thyroid function. We examined two types of ANN architecture and assessed their robustness in the face of diagnostic noise. The thyroid function data we used had previously been studied by multivariate statistical methods and a variety of pattern-recognition techniques. The total data set comprised 392 cases that had been classified according to both thyroid function and 19 clinical categories. All cases had a complete set of results of six laboratory tests (total thyroxine, free thyroxine, triiodothyronine, triiodothyronine uptake test, thyrotropin, and thyroxine-binding globulin). This data set was divided into subsets used for training the networks and for testing their performance; the test subsets contained various proportions of cases with diagnostic noise to mimic real-life diagnostic situations. The networks studied were a multilayer perceptron trained by back-propagation, and a learning vector quantization network. The training data subsets were selected according to two strategies: either training data based on cases with extreme values for the laboratory tests with randomly selected nonextreme cases added, or training cases from very pure functional groups. Both network architectures were efficient irrespective of the type of training data. The correct allocation of cases in test data subsets was 96.4-99.7% when extreme values were used for training and 92.7-98.8% when only pure cases were used.

scholarly journals Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation

2005 ◽  
Vol 2 (1) ◽  
pp. 365-415 ◽  
Author(s):  
N. J. de Vos ◽  
T. H. M. Rientjes
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Goodness Of Fit ◽  
Model Performance ◽  
Performance Measure ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Trade Offs ◽  
Hourly Data ◽  
Artificial Neural

Abstract. The application of Artificial Neural Networks (ANNs) on rainfall-runoff modelling needs to be researched more extensively in order to appreciate and fulfil the potential of this modelling approach. This paper reports on the application of multi-layer feedforward ANNs for rainfall-runoff modelling in the Geer catchment (Belgium) using both daily and hourly data. The good daily forecast results indicate that ANNs can be considered alternatives for traditional rainfall-runoff modelling approaches. However, investigation of the forecasts based on hourly data reveal a constraint that has hitherto been neglected by hydrologists. A timing error occurs due to a dominating autoregressive component that is introduced by using previous runoff values as ANN model input. The reason for the popular practice of using these previous runoff data is that this information indirectly represents the hydrological state of the catchment. Two possible solutions to this timing problem are discussed. Firstly, several alternatives for representation of the hydrological state are presented: moving averages over the previous discharge and over the previous rainfall, and the output of the simple GR4J model component for soil moisture. A combination of these various hydrological state representators produces good results in terms of timing, but the overall goodness of fit is not as good as the simulations with previous runoff data. Secondly, the use of a combination of multiple measures of model performance during ANN training is suggested, since not all differences between modelled and observed hydrograph characteristics such as timing, volume, and absolute values can be adequately expressed by a single performance measure. The possible undervaluation of timing errors by the commonly-used squared-error-based functions is a clear example of this inability.

Prediction of hexagonal lattice parameters of various apatites by artificial neural networks

2010 ◽  
Vol 43 (4) ◽  
pp. 769-779 ◽  
Author(s):  
Umit Kockan ◽  
Zafer Evis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hexagonal Lattice ◽  
Correlation Coefficients ◽  
Activation Function ◽  
Lattice Parameters ◽  
Training Data ◽  
Ionic Radii ◽  
Data Set ◽  
Artificial Neural

In this study, the hexagonal lattice parameters of apatite compounds,M10(TO4)6X2, whereMis Na+, Ca2+, Ba2+, Cd2+, Pb2+, Sr2+, Mn2+, Zn2+, Eu2+, Nd3+, La3+or Y3+,Tis As+5, Cr+5, P5+, V5+or Si+4, andXis F−, Cl−, OH−or Br−, were predicted from their ionic radii by artificial neural networks. A multilayer perceptron network was used for training and the best results were obtained with a Bayesian regularization method. Four neurons were used in the hidden layer, utilizing a tangent sigmoid activation function, while one neuron was used in the output layer with a pure linear function. The results of the training showed that the correlation coefficients for the hexagonal lattice parameters were 0.991 for the training data set, which is very close to unity, demonstrating that the learning process was successful. In addition, the average errors of the predicted lattice parameters were less than 1% for the data set prepared with single ions at theM,TandXsites, as well as for apatites with coupled substitutions involving up to three different ions at each site. Simple mathematical formulae were derived for the prediction of lattice parameters using average ionic radii as independent variables.

scholarly journals Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation

2005 ◽  
Vol 9 (1/2) ◽  
pp. 111-126 ◽  
Author(s):  
N. J. de Vos ◽  
T. H. M. Rientjes
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Goodness Of Fit ◽  
Model Performance ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Trade Offs ◽  
Hourly Data ◽  
Artificial Neural ◽  
Daily Forecast

Abstract. The application of Artificial Neural Networks (ANNs) in rainfall-runoff modelling needs to be researched more extensively in order to appreciate and fulfil the potential of this modelling approach. This paper reports on the application of multi-layer feedforward ANNs for rainfall-runoff modelling of the Geer catchment (Belgium) using both daily and hourly data. The daily forecast results indicate that ANNs can be considered good alternatives for traditional rainfall-runoff modelling approaches, but the simulations based on hourly data reveal timing errors as a result of a dominating autoregressive component. This component is introduced in model simulations by using previously observed runoff values as ANN model input, which is a popular method for indirectly representing the hydrological state of a catchment. Two possible solutions to this problem of lagged predictions are presented. Firstly, several alternatives for representation of the hydrological state are tested as ANN inputs: moving averages over time of observed discharges and rainfall, and the output of the simple GR4J model component for soil moisture. A combination of these hydrological state representers produces good results in terms of timing, but the overall goodness of fit is not as good as the simulations with previous runoff data. Secondly, the possibility of using multiple measures of model performance during ANN training is mentioned.

scholarly journals Inverse calculation of strain profiles from ETDR measurements using artificial neural networks

2017 ◽  
Vol 6 (2) ◽  
pp. 389-394 ◽  
Author(s):  
Robin Höhne ◽  
Pawel Kostka ◽  
Niels Modler
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Time Domain Reflectometry ◽  
Training Data ◽  
Ann Model ◽  
Spatially Resolved ◽  
Quantitative Accuracy ◽  
Measurement Principle ◽  
Artificial Neural ◽  
Line Design

Abstract. A novel carbon fibre sensor is developed for the spatially resolved strain measurement. A unique feature of the sensor is the fibre-break resistive measurement principle and the two-core transmission line design. The electrical time domain reflectometry (ETDR) is used in order to realize a spatially resolved measurement of the electrical parameters of the sensor. In this contribution, the process of mapping between the ETDR signals to the existing strain profile is described. Artificial neural networks (ANNs) are used to solve the inverse electromagnetic problem. The investigations were carried out with a sensor patch in a cantilever arm configuration. Overall, 136 experiments with varying strain distribution over the sensor length were performed to generate the necessary training data to learn the ANN model. The validation of the ANN highlights the feasibility as well as the current limits concerning the quantitative accuracy of mapping ETDR signals to strain profiles.

Hydrological modelling using artificial neural networks

2001 ◽  
Vol 25 (1) ◽  
pp. 80-108 ◽  
Author(s):  
C. W. Dawson ◽  
R. L. Wilby
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Performance ◽  
Model Complexity ◽  
Network Architectures ◽  
Training Algorithms ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Validation Data ◽  
Artificial Neural

This review considers the application of artificial neural networks (ANNs) to rainfall-runoff modelling and flood forecasting. This is an emerging field of research, characterized by a wide variety of techniques, a diversity of geographical contexts, a general absence of intermodel comparisons, and inconsistent reporting of model skill. This article begins by outlining the basic principles of ANN modelling, common network architectures and training algorithms. The discussion then addresses related themes of the division and preprocessing of data for model calibration/validation; data standardization techniques; and methods of evaluating ANN model performance. A literature survey underlines the need for clear guidance in current modelling practice, as well as the comparison of ANN methods with more conventional statistical models. Accordingly, a template is proposed in order to assist the construction of future ANN rainfall-runoff models. Finally, it is suggested that research might focus on the extraction of hydrological ‘rules’ from ANN weights, and on the development of standard performance measures that penalize unnecessary model complexity.

Application of Artificial Neural Networks for Monitoring and Optimum Operation Prediction of Solar Hybrid MGT Systems

2019 ◽  
Author(s):  
Homam Nikpey Somehsaraei ◽  
Davide Iaria ◽  
Jafar Al Zaili ◽  
Mohsen Assadi ◽  
Abdulnaser Sayma ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Distributed Generation ◽  
Gas Turbines ◽  
Solar Irradiation ◽  
Ann Model ◽  
Data Set ◽  
Optimum Operation ◽  
Multi Objective ◽  
Artificial Neural

Abstract Hybrid energy system consisting of a parabolic dish solar concentrator and a micro gas turbine (MGT) has been considered as promising distributed generation technology, since it can be operated as a stand-alone system for power or combined heat and power (CHP) applications in remote areas with no connection to the grid. The main concern when it comes to distributed generation is the ability of maintaining high availability. Therefore, given the intermittency of the solar resource, the availability of consistent and computationally fast tools for modelling and monitoring of solar micro gas turbines is essential for adequate and optimum operation. This paper presents the application of artificial neural networks (ANNs) for performance prediction and monitoring of a hybrid solar MGT system. For this purpose, a validated in-house tool, developed for evaluating the performance of solar-hybrid MGT, was used to generate simulated data at various operational conditions by varying solar irradiation and ambient conditions. The obtained data was used to train the ANN model. The prediction accuracy of the ANN model was tested using a data set, which were not used during the training process. The results showed that the ANN model can predict the solar hybrid MGT performance with high accuracy and could serve as an accurate baseline model for monitoring applications. Finally, the developed ANN model was integrated with an optimization algorithm. A case study was conducted using the developed ANN model for multi-objective optimization of the hybrid solar MGT. By varying turbine inlet temperature and rotational speed, the system performance at part load operation were analysed resulting in a Pareto front for maximum electrical efficiency and minimal operational cost. Multi-objective genetic algorithm (GA) based on controlled elitism concept was applied to find the optimum solution.

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