An innovative method for dynamic update of initial water table in XXT model based on neural network technique

Liu, Shuang; Xu, Jingwen; Jun, Zhao; Xie, Xingmei; Zhang, Wanchang

doi:10.1016/j.asoc.2013.06.024

Cited by 9 publications

(10 citation statements)

References 48 publications

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“…The periods for calibration and validation have been chosen according to data availability. Earlier studies show the feasibility of using these data for simulation experiments (Xu et al ., 2009; Liu et al ., 2013a).…”

Section: Methodsmentioning

confidence: 99%

“…Many studies on streamflow modeling improvement were based on this model (Takeuchi et al, 2008;Bouilloud et al, 2010;Xu et al, 2012). Recently, artificial intelligence technology was also combined with such simple models (Srinivasulu and Jain, 2009;Liu et al, 2013a;Nikoo et al, 2016). However, a single model, even a great distributed model, cannot be able to well depict the runoff variation due to uncertainties in both model schemes and parameters (Roundy et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Many studies on streamflow modeling improvement were based on this model (Takeuchi et al ., 2008; Bouilloud et al ., 2010; Xu et al ., 2012). Recently, artificial intelligence technology was also combined with such simple models (Srinivasulu and Jain, 2009; Liu et al ., 2013a; Nikoo et al ., 2016).…”

Section: Introductionmentioning

confidence: 99%

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Ensemble learning of daily river discharge modeling for two watersheds with different climates

Zhang²,

Liu

et al. 2020

Atmospheric Science Letters

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In order to reduce the uncertainties and improve the river discharge modeling accuracy, several topography-based hydrological models (TOPMODEL), generated by different combinations of parameters, were incorporated into an ensemble learning framework with the boosting method. Both the Baohe River Basin (BRB) with humid climate, and the Linyi River Basin (LRB) with semiarid climate were chosen for model testing. Observed daily precipitation, pan evaporation and stream flow data were used for model development and testing. Different Nash-Sutcliffe efficiency coefficients, the coefficient of determination and the Root Mean Square Error were adopted to implement a comprehensive assessment on model performances. Testing results indicated that ensemble learning method could improve the modeling accuracy by comparing with the best single TOPMODEL. During the validation periods, the boosting method could increase the modeling accuracy by 9 and 16% for BRB and LRB, respectively. The ensemble method significantly narrowed the gap of model performances over watersheds with different climatic conditions. Hence, using the ensemble learning to enhance the feasibility of hydrological models for different climatic regions is promising.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ensemble learning of daily river discharge modeling for two watersheds with different climates

Zhang²,

Liu

et al. 2020

Atmospheric Science Letters

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“…This is also a common approach in most of the data mining techniques based on artificial intelligence such as neural network and genetic programming [10][11][12][13][14][15][16][17][18][19][20]. The construction of the model takes place by adaptive learning over the training set and the performance of the constructed model is then appraised using the validation set.…”

Section: Epr Proceduresmentioning

confidence: 99%

“…W/C=0.4) for all the samples. The concrete by training sets of input and output data [11,12]. ANNs have the ability to model complex, nonlinear processes without having to assume the form of the relationship between input and output variables [13,14].…”

Section: Introductionmentioning

confidence: 99%

An evolutionary approach to modelling concrete degradation due to sulphuric acid attack

Alani

Faramarzi

2014

Applied Soft Computing

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Please cite this article as: A.M. Alani, An evolutionary approach to modelling concrete degradation due to sulphuric acid attack, Applied Soft Computing Journal (2014), http://dx.doi.org/10. 1016/j.asoc.2014.08.044 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. EPR ProcessPage 2 of 38 A c c e p t e d M a n u s c r i p t 2 Highlights 1-We present a new evolutionary approach for modelling the degradation of concrete 2-The developed models predict the mass loss of concrete due to acid attack 3-Optimum concrete mix to maximise resistance against degradation is determined Abstract:Concrete corrosion due to sulphuric acid attack is known to be one of the main contributory factors for degradation of concrete sewer pipes. This paper proposes to use a novel data mining technique, namely, evolutionary polynomial regression (EPR), to predict degradation of concrete subject to sulphuric acid attack. A comprehensive dataset from literature is collected to train and develop an EPR model for this purpose. The results show that the EPR model can successfully predict mass loss of concrete specimens exposed to sulphuric acid.Parametric studies show that the proposed model is capable of representing the degree to which individual contributing parameters can affect the degradation of concrete. The developed EPR model is compared with a model based on artificial neural network (ANN) and the advantageous of the EPR approach over ANN is highlighted. In addition, based on the developed EPR model and using an optimisation technique, the optimum concrete mixture to provide maximum resistance against sulphuric acid attack has been identified.

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Efficiency enhancement of a process-based rainfall–runoff model using a new modified AdaBoost.RT technique

Liu

Jun

et al. 2014

Applied Soft Computing

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An innovative method for dynamic update of initial water table in XXT model based on neural network technique

Cited by 9 publications

References 48 publications

Ensemble learning of daily river discharge modeling for two watersheds with different climates

Ensemble learning of daily river discharge modeling for two watersheds with different climates

An evolutionary approach to modelling concrete degradation due to sulphuric acid attack

Efficiency enhancement of a process-based rainfall–runoff model using a new modified AdaBoost.RT technique

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