scholarly journals Neural network modelling of non-linear hydrological relationships

2007 ◽  
Vol 11 (5) ◽  
pp. 1563-1579 ◽  
Author(s):  
R. J. Abrahart ◽  
L. M. See
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Network Models ◽  
Target Surface ◽  
General Information ◽  
Neural Network Models ◽  
Network Modelling ◽  
Neural Network Modelling ◽  
Linear Relationships ◽  
Non Linear

Abstract. Two recent studies have suggested that neural network modelling offers no worthwhile improvements in comparison to the application of weighted linear transfer functions for capturing the non-linear nature of hydrological relationships. The potential of an artificial neural network to perform simple non-linear hydrological transformations under controlled conditions is examined in this paper. Eight neural network models were developed: four full or partial emulations of a recognised non-linear hydrological rainfall-runoff model; four solutions developed on an identical set of inputs and a calculated runoff coefficient output. The use of different input combinations enabled the competencies of solutions developed on a reduced number of parameters to be assessed. The selected hydrological model had a limited number of inputs and contained no temporal component. The modelling process was based on a set of random inputs that had a uniform distribution and spanned a modest range of possibilities. The initial cloning operations permitted a direct comparison to be performed with the equation-based relationship. It also provided more general information about the power of a neural network to replicate mathematical equations and model modest non-linear relationships. The second group of experiments explored a different relationship that is of hydrological interest; the target surface contained a stronger set of non-linear properties and was more challenging. Linear modelling comparisons were performed against traditional least squares multiple linear regression solutions developed on identical datasets. The reported results demonstrate that neural networks are capable of modelling non-linear hydrological processes and are therefore appropriate tools for hydrological modelling.

scholarly journals Neural network emulation of a rainfall-runoff model

2007 ◽  
Vol 4 (1) ◽  
pp. 287-326 ◽  
Author(s):  
R. J. Abrahart ◽  
L. M. See
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Network Models ◽  
Exact Equation ◽  
Rainfall Runoff ◽  
Neural Network Models ◽  
Linear Relationships ◽  
Non Linear ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. The potential of an artificial neural network to perform simple non-linear hydrological transformations is examined. Four neural network models were developed to emulate different facets of a recognised non-linear hydrological transformation equation that possessed a small number of variables and contained no temporal component. The modeling process was based on a set of uniform random distributions. The cloning operation facilitated a direct comparison with the exact equation-based relationship. It also provided broader information about the power of a neural network to emulate existing equations and model non-linear relationships. Several comparisons with least squares multiple linear regression were performed. The first experiment involved a direct emulation of the Xinanjiang Rainfall-Runoff Model. The next two experiments were designed to assess the competencies of two neural solutions that were developed on a reduced number of inputs. This involved the omission and conflation of previous inputs. The final experiment used derived variables to model intrinsic but otherwise concealed internal relationships that are of hydrological interest. Two recent studies have suggested that neural solutions offer no worthwhile improvements in comparison to traditional weighted linear transfer functions for capturing the non-linear nature of hydrological relationships. Yet such fundamental properties are intrinsic aspects of catchment processes that cannot be excluded or ignored. The results from the four experiments that are reported in this paper are used to challenge the interpretations from these two earlier studies and thus further the debate with regards to the appropriateness of neural networks for hydrological modelling.

scholarly journals Extreme learning machines for weather-based modelling of silk cocoon production

2022 ◽  
Vol 24 (1) ◽  
Author(s):  
PRAMIT PANDIT ◽  
BISHVAJIT BAKSHI ◽  
SHILPA M.
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Extreme Learning Machines ◽  
Linear Regression Models ◽  
Weather Variables ◽  
Neural Network Models ◽  
Cocoon Production ◽  
Silk Cocoon ◽  
Learning Machines ◽  
Non Linear

In spite of the immense popularity and sheer power of the neural network models, their application in sericulture is still very much limited. With this backdrop, this study evaluates the suitability of neural network models in comparison with the linear regression models in predicting silk cocoon production of the selected six districts (Kolar, Chikballapur, Ramanagara, Chamarajanagar, Mandya and Mysuru) of Karnataka by utilising weather variables for ten consecutive years (2009-2018). As the weather variables are found to be correlated, principal components are obtained and fed into the linear (principal component regression) and non-linear models (back propagation-artificial neural network and extreme learning machine) as inputs. Outcomes emanated from this experiment have revealed the clear advantages of employing extreme learning machines (ELMs) for weather-based modelling of silk cocoon production. Application of ELM would be particularly useful, when the relation between production and its attributing characters is complex and non-linear.

scholarly journals Comparison of Non Linear Models and Artificial Neural Network to Describe the Liveweight from Birth to Maturity in Female Jersey Cattle

2021 ◽  
pp. 114-119
Author(s):  
D. O. Omoniwa ◽  
J. E. T. Akinsola ◽  
R. O. Okeke ◽  
J. M. Madu ◽  
D. S. Bunjah Umar
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Inflection Point ◽  
Linear Models ◽  
Network Models ◽  
Linear Functions ◽  
Growth Data ◽  
Neural Network Models ◽  
Jersey Cattle ◽  
Non Linear

Evaluation of growth data is an important strategy to manage gross feed requirement in female Jersey cattle in the New Derived Guinea Savannah Zone of Nigeria. Two non-linear functions (Gompertz and Logistic) and Neural network models were used to fit liveweight (LW)-age data using the non linear procedure of JMP statistical software. Data used for this study were collected from 150 Jersey female cattle in Shonga Dairy Farm, Kwara, State from 2010-2018. The Neural network function showedthe best goodness of fit. Both the Gompertz and Logistic functions overestimated LW at birth, 3, 36, 48, 60 and 72months respectively. NN function overestimated the LW at 0, 3, 24, 36 and 72 months. The Gompertzfunction had the best estimation of asymptotic weight (649.51 kg) with average absolute growth rate (0.061 kg/day).The inflection point was 15.95, 9.55 and 34.5 months in Logistic, Gompertz and neural network models, respectively. A strong and positive correlation was observed between asymptote and inflection point in Gompertz functions. The metrics of goodness of fit criteria (R2 and RMSE), showed that NN with multilayer perceptron was superior to the other models but Gompertz model, was best in its ability to approximate complex functions of growth curve parametersin female Jersey cattle.

scholarly journals Predicting Credit Rating Migration Employing Neural Network Models

2020 ◽  
pp. 65-82
Author(s):  
Michael D'Rosario ◽  
Calvin Hsieh
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Credit Rating ◽  
Predictive Accuracy ◽  
Estimation Method ◽  
Network Models ◽  
Legal Framework ◽  
Neural Network Models ◽  
Network Modelling ◽  
Extant Literature

Credit rating migration ranks amongst the most pertinent issues concerning institutional lenders and investors alike. There are a number of studies that have employed both parametric and non-parametric methodologies to forecast credit rating migration, employing machine learning methods; and notably, artificial intelligence methods becoming increasingly popular. The present study extends upon research within the extant literature employing a novel estimation method, a neural network modelling technique, herewith the MPANN (multi-layer neural network). Consistent with the extant literature, the present article identifies that the legal framework and system of taxation enacted within a polity are pertinent to predicting rating migration. However, extending upon traditional estimation techniques the study identifies that a number of different model calibrations achieve greater predictive accuracy than traditional parametric regression. Notably, the method is able to achieve superior goodness of fit and predictive accuracy in determining credit rating migration than models employed within the extant literature.

Modeling and Prediction of the CNY Exchange Rates Using RBF Neural Networks versus GARCH Models

2010 ◽  
Vol 39 ◽  
pp. 375-382 ◽  
Author(s):  
Zhao Cheng Liu ◽  
Xi Yu Liu ◽  
Zi Ran Zheng
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Exchange Rates ◽  
Transfer Functions ◽  
Rbf Neural Network ◽  
Network Models ◽  
Garch Models ◽  
Rbf Neural Networks ◽  
Neural Network Models

The CNY exchange rates can be viewed as financial time series which are characterized by high uncertainty, nonlinearity and time-varying behavior. Predictions for CNY exchange rates of GBP-CNY and USD-CNY were carried out respectively by means of RBF neural network forecasters and GARCH models. GARCH is a mechanism that includes past variances in the explanation of future variances and a time-series technique that we use to model the serial dependence of volatility. The detailed design of architectures of RBF neural network models, transfer functions of the hidden layer nodes, input vectors and output vectors were made with many tests. While experimental results show that the performance of RBF neural networks for forecasting spot CNY exchange rates is better than that of GARCH, both of them are acceptable and effective especially in short term predictions.

scholarly journals Predicting Credit Rating Migration Employing Neural Network Models

2018 ◽  
Vol 9 (4) ◽  
pp. 70-85
Author(s):  
Michael D'Rosario ◽  
Calvin Hsieh
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Credit Rating ◽  
Predictive Accuracy ◽  
Estimation Method ◽  
Network Models ◽  
Legal Framework ◽  
Neural Network Models ◽  
Network Modelling ◽  
Extant Literature

Credit rating migration ranks amongst the most pertinent issues concerning institutional lenders and investors alike. There are a number of studies that have employed both parametric and non-parametric methodologies to forecast credit rating migration, employing machine learning methods; and notably, artificial intelligence methods becoming increasingly popular. The present study extends upon research within the extant literature employing a novel estimation method, a neural network modelling technique, herewith the MPANN (multi-layer neural network). Consistent with the extant literature, the present article identifies that the legal framework and system of taxation enacted within a polity are pertinent to predicting rating migration. However, extending upon traditional estimation techniques the study identifies that a number of different model calibrations achieve greater predictive accuracy than traditional parametric regression. Notably, the method is able to achieve superior goodness of fit and predictive accuracy in determining credit rating migration than models employed within the extant literature.

The Use of Regression and Neural Network Modelling in Production Monitoring of an Industrial Enterprise

2021 ◽  
Vol 25 (5) ◽  
pp. 58-64
Author(s):  
A.R. Kholova ◽  
Yu.S. Vozhdaeva ◽  
I.A. Melnitskiy ◽  
R.I. Kiekbayev ◽  
P.V. Serebryakov ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Treatment ◽  
River Water ◽  
Statistical Significance ◽  
Neural Network Models ◽  
Treatment Technology ◽  
Network Modelling ◽  
Test Set ◽  
Neural Network Modelling ◽  
True Values

Regression and neural network model for predicting the required dose of coagulant, depending on the quality of river water supplied for water treatment, are considered, their comparative analysis is carried out. For modelling and forecasting, statistical data collected for the period from 2005 to nowadays. Regression models were built on the true values of the factors (water quality indicators) and on their first differences to eliminate the trend in the series. For the models built on the true values, the statistical significance, was confirmed, high values of the coefficient of the determination were obtained, the values of the approximation errors were 22–25 %. In neural network modelling, networks of the multilayer perception were used. Generalization error on the test set when using other type of networks (RBF-networks, Elman networks), was significant above. Computational experiments have shown that, in general, the accuracy of neural network models is higher than regression ones. The average learning error was 7–9 %, the error on the test set increases to 12–16 %. The reliability of the forecast is increased by training the network on more recent data and using a larger set of facts. An increase in the influence of indicators of permanganate oxidability and colour of the initial river water on the dose of reagents with a simultaneous decrease in the degree of influence of alkalinity over the last five-year period was revealed. This confirms the need to periodically update data for building models. Selected models recommended for implementation in industrial monitoring of water treatment technology at the enterprise.

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