Linear genetic programming for time-series modelling of daily flow rate

Güven, Aytaç

doi:10.1007/s12040-009-0022-9

Cited by 136 publications

(51 citation statements)

References 42 publications

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“…A typical LGP program would have registers and constants from pre-defined sets. Various applications of LGP's have been implemented, including web usage mining [34], formulation of the compressive strength of concrete cylinders [35], time-series modelling [36], and intrusion detection [37], [38].…”

Section: Genetic Programmingmentioning

confidence: 99%

SPAM Detection: Naïve Bayesian Classification and RPN Expression-Based LGP Approaches Compared

Meli

Oplatková

2016

Advances in Intelligent Systems and Computing

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Abstract. An investigation is performed of a machine learning algorithm and the Bayesian classifier in the spam-filtering context. The paper shows the advantage of the use of Reverse Polish Notation (RPN) expressions with feature extraction compared to the traditional Naïve Bayesian classifier used for spam detection assuming the same features. The performance of the two is investigated using a public corpus and a recent private spam collection, concluding that the system based on RPN LGP (Linear Genetic Programming) gave better results compared to two popularly used open source Bayesian spam filters.

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Section: Genetic Programmingmentioning

confidence: 99%

SPAM Detection: Naïve Bayesian Classification and RPN Expression-Based LGP Approaches Compared

Meli

Oplatková

2016

Advances in Intelligent Systems and Computing

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“…An operand or operator of an instruction is changed by mutation into another symbol over the same set. The readers are referred to [7] and [8] for further details.…”

Section: Genetic Programmingmentioning

confidence: 99%

“…The details are not produced here to save the space. Linear Genetic Programming technique was used to predict daily river discharge one day ahead using previous values of the same at Schuylkill River at Berne, PA, USA [8].…”

Section: Applications In Hydrologymentioning

confidence: 99%

Genetic Programming: A Novel Computing Approach in Modeling Water Flows

Londhe¹,

Dixit²

2012

Genetic Programming - New Approaches and Successful Applications

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“…Each individual program in LGP is represented by a variable-length sequence of simple C language instructions. These instructions operate on one or more registers (r[i]) or constants (c) from predefined sets (Brameier, 2004;Guven, 2009). The function set of the system are composed of arithmetic operations {+, -,…”

Section: Linear Genetic Programming Model Thementioning

confidence: 99%

Improving the performance of data-driven techniques through data pre-processing for modelling daily reservoir inflow

Jothiprakash

Kote

2011

Hydrological Sciences Journal

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The effect of data pre-processing while developing artificial intelligence (AI) -based data-driven techniques, such as artificial neural networks (ANN), model trees (MT) and linear genetic programming (LGP), is studied for Pawana Reservoir in Maharashtra, India. The daily one-step-ahead inflow forecasts are compared with flows generated from a univariate autoregressive integrated moving average (ARIMA) model. For the full-year data series, a large error is found mainly due to the occurrence of zero values, since the reservoir is located in an intermittent river. Hence, all the techniques are evaluated using two data series: 18 years of daily full-year inflow data (from 1 January to 31 December); and 18 years of daily monsoon season inflow data (from 1 June to 31 October) to take into account the intermittent nature of the data. The relevant range of inputs for each category is selected based on autocorrelation and partial autocorrelation analyses of the inflow series. Conventional preprocessing methods, such as transformation and/or normalization of data, do not perform well because of the large variation in magnitudes, as well as the many zero values (65% of the full-year data series). Therefore, the input data are pre-processed into un-weighted moving average (MA) series of 3 days, 5 days and 7 days. The 3-day MA series performs better, maintaining the peak inflow pattern as in the actual data series, while the coarser-scale (5-day and 7-day) MA series reduce the peak inflow pattern, leading to more errors in peak inflow prediction. The results indicate that AI methods are powerful tools for modelling the daily flow time series with appropriate data preprocessing, in spite of the presence of many zero values. The time-lagged recurrent network (TLRN) ANN modelling technique applied in this study maps the inflow forecasting in a better way than the standard multilayer perceptron (MLP) neural networks, especially in the case of the seasonal data series. The MT technique performs equally well for low and medium inflows, but fails to predict the peak inflows. However, LGP outperforms the other AI models, and also the ARIMA model, for all inflow magnitudes. In the LGP model, the daily full-year data series with more zero inflow values performs better than the daily seasonal models.Key words data pre-processing; time-lagged recurrent network; model tree; linear genetic programming; moving average; transformation; full-year inflow data; seasonal inflow; Pawana Reservoir, India Amélioration de la performance de techniques conditionnées par les données par pré-traitement pour la modélisation de l'apport journalier d'un réservoir Résumé L'effet du pré-traitement des données à travers le développement des techniques d'intelligence artificielle (IA) conditionnées par les données, telles que les réseaux de neurones artificiels (RNA), les arbres modèles (MT) et la programmation génétique linéaire (PGL), est étudié pour le Réservoir Pawana dans le Maharashtra, Inde. Les prévisions à un jour des apports journaliers so...

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Linear genetic programming for time-series modelling of daily flow rate

Cited by 136 publications

References 42 publications

SPAM Detection: Naïve Bayesian Classification and RPN Expression-Based LGP Approaches Compared

SPAM Detection: Naïve Bayesian Classification and RPN Expression-Based LGP Approaches Compared

Genetic Programming: A Novel Computing Approach in Modeling Water Flows

Improving the performance of data-driven techniques through data pre-processing for modelling daily reservoir inflow

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