Prediction of Soil–Water Characteristic Curve Using Genetic Programming

Johari, A.; Habibagahi, Ghassem; Ghahramani, A.

doi:10.1061/(asce)1090-0241(2006)132:5(661)

Cited by 171 publications

(79 citation statements)

References 7 publications

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“…The GP approach was also 82 employed by Johari et al (2006) to predict the soil-water characteristic curve of soils. GP is 83 employed for modelling and prediction of algal blooms in Tolo Harbour, Hong Kong (Muttil and 84 Chau, 2006) and the results indicated good predictions of long-term trends in algal biomass.…”

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confidence: 99%

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

Wang

Chau

Cheng

et al. 2009

Journal of Hydrology

689

308

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Abstract. Developing a hydrological forecasting model based on past records is crucial to 23 effective hydropower reservoir management and scheduling. Traditionally, time series analysis and 24 modeling is used for building mathematical models to generate hydrologic records in hydrology 25 and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of 26 analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to 27 apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive 28 moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive 29 neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and 30 support vector machine (SVM) method are examined using the long-term observations of monthly 31 river flow discharges. The four quantitative standard statistical performance evaluation measures, 32 the coefficient of correlation (R), Nash-Sutcliffe efficiency coefficient (E), root mean squared 33 error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the 34 performances of various models developed. Two case study river sites are also provided to 35 illustrate their respective performances. The results indicate that the best performance can be 36 obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and 37 validation phases. 38 39

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mentioning

confidence: 99%

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

Wang

Chau

Cheng

et al. 2009

Journal of Hydrology

689

308

View full text Add to dashboard Cite

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“…Several researchers (e.g., [34,50,[101][102][103][104]) have recently used the GP technique as an alterative to ANNs in order to obtain greatly simplified formulae for some geotechnical engineering problems. GP is a computing method that attempts to mimic the biological evolution of living organisms.…”

Section: Model Transparency and Knowledge Extractionmentioning

confidence: 99%

Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications

Shahin

Jaksa

Maier

2009

Advances in Artificial Neural Systems

106

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Artificial neural networks (ANNs) are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex engineering problems that are beyond the computational capability of classical mathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of geotechnical engineering problems. Despite the increasing number and diversity of ANN applications in geotechnical engineering, the contents of reported applications indicate that the progress in ANN development and procedures is marginal and not moving forward since the mid-1990s. This paper presents a brief overview of ANN applications in geotechnical engineering, briefly provides an overview of the operation of ANN modeling, investigates the current research directions of ANNs in geotechnical engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty.

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“…These techniques include Finite-Di erence Method (FDM), Finite-Element Method (FEM), Finite Volume Method (FVM), hp-FEM and time splitting method [2][3][4][5][6][7][8][9][10][11]. Analytical solutions, on the other hand, are mainly o ered for one-dimensional ow of water through the soil and for restrictive boundary and initial conditions.…”

Section: Introductionmentioning

confidence: 99%

Innovative Analytical Solutions to 1, 2 and 3D Water Infiltration into Unsaturated Soils for Initial-Boundary Value Problems

2017

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Abstract. Fluid in ltration into unsaturated soil is of vital signi cance from many perspectives. Mathematically, such transient in ltrations are described by Richards' equation: a nonlinear parabolic Partial Di erential Equation (PDE) with limited analytical solutions in the literature. The current study uses separation of variables and Fourier series expansion techniques and presents new analytical solutions to the equation in one, two, and three dimensions subject to various boundary and initial conditions. Solutions for 1D horizontal and vertical water in ltration are derived and compared to numerical nite-di erence method solutions, whereby both solutions are shown to coincide well with one another. Solutions to 2 and 3D vertical water in ltration are derived for constant, no-ow, and sinusoidal boundary and initial conditions. The presented analytical solutions are such that both steady and unsteady solutions may be obtained from a single closed form solution. The solutions may be utilized to test numerical models that use di erent computational techniques.

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Prediction of Soil–Water Characteristic Curve Using Genetic Programming

Cited by 171 publications

References 7 publications

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications

Innovative Analytical Solutions to 1, 2 and 3D Water Infiltration into Unsaturated Soils for Initial-Boundary Value Problems

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