Predicting stable alluvial channel profiles using emotional artificial neural networks

Abstract Accurate prediction of the cross-sectional profile of stable alluvial channels is very important for hydraulic engineers in the design and construction of natural channels in urban watersheds. This paper presents the first-time application of Emotional Artificial Neural Network (EANN) modeling to predict the stable cross-sectional geometry of alluvial channel profiles. EANN applies emotional stimulus parameters to improve the network’s training process. The key input parameters of the EANN model include modified Froude number (Fr m ), dimensionless mean sediment size (d*) and dimensionless lateral distance from the channel centerline (x*), and the output (or target) is the dimensionless vertical boundary level of the channel cross section (y*). Several observational datasets are used with different hydraulic and geometric conditions to verify and validate the model. Feed forward neural network (FFNN) and Gene expression programming (GEP) models are also designed to make a better evaluation and comparison of EANN performance. Furthermore, the prediction accuracy of the new EANN model is compared with several popular existing relationships related to previous researchers. The results declared that the EANN model with low error values of correlation coefficient (R) equal to 0.99 and root mean square error (RMSE) equal to 0.00058 is more accurate than FFNN model (R = 0.76 and RMSE = 0.231), which represents a significant improvement in EANN performance compared to classical FFNN. It is found that the EANN model has the lowest RMSE compared to all previous models (RMSE = 0.13 for the best previous model related to Diplas, 1990). Uncertainty analysis is performed to evaluate the quantitative performance of the new EANN and existing models. The results indicate that EANN has the lowest Width of the Confidence Bounds (WCB) and the lowest Mean Error of Predictions (MEP) of ± 0.00004 and -0.00041 respectively compared to FFNN and GEP and also previous models. Therefore, the new EANN model is proposed as a superior alternative to existing models for river engineering applications in the design of stable natural alluvial channels.

[1]  Amir Hossein Zaji,et al.  Design of modified structure multi-layer perceptron networks based on decision trees for the prediction of flow parameters in 90° open-channel bends , 2016 .

[2]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[3]  Subhasish Dey.  Bank profile of threshold channels : A simplified approach , 2001 .

[4]  Adnan Khashman,et al.  A Modified Backpropagation Learning Algorithm With Added Emotional Coefficients , 2008, IEEE Transactions on Neural Networks.

[5]  Aminuddin Ab. Ghani.  Sediment transport in sewers , 1993 .

[6]  Hassan I. Mohamed,et al.  Design of alluvial Egyptian irrigation canals using artificial neural networks method , 2013 .

[7]  Mohammad Reza Nikoo,et al.  Development of expert systems for the prediction of scour depth under live-bed conditions at river confluences: Application of different types of ANNs and the M5P model tree , 2015, Appl. Soft Comput..

[8]  H. Md. Azamathulla,et al.  Gene-expression programming for transverse mixing coefficient , 2012 .

[9]  Hossein Bonakdari,et al.  Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels , 2016 .

[10]  G. Parker.  Self-formed straight rivers with equilibrium banks and mobile bed. Part 2. The gravel river , 1978, Journal of Fluid Mechanics.

[11]  Caro Lucas,et al.  Learning based brain emotional intelligence as a new aspect for development of an alarm system , 2008, Soft Comput..

[12]  Amir Hossein Zaji,et al.  Simulation of open channel bend characteristics using computational fluid dynamics and artificial neural networks , 2015 .

[13]  Bahram Gharabaghi,et al.  Prediction of Timing of Watermain Failure Using Gene Expression Models , 2016, Water Resources Management.

[14]  Ehsan Lotfi,et al.  Adaptive brain emotional decayed learning for online prediction of geomagnetic activity indices , 2014, Neurocomputing.

[15]  Ramazan Akkaya,et al.  Training data optimization for ANNs using genetic algorithms to enhance MPPT efficiency of a stand-alone PV system , 2012, Turkish Journal of Electrical Engineering and Computer Sciences.

[16]  Hossein Bonakdari,et al.  Comparative analysis of GMDH neural network based on genetic algorithm and particle swarm optimization in stable channel design , 2017, Appl. Math. Comput..

[17]  Adnan Khashman,et al.  Application of an emotional neural network to facial recognition , 2009, Neural Computing and Applications.

[18]  Ehsan Lotfi,et al.  Practical emotional neural networks , 2014, Neural Networks.

[19]  Bahram Gharabaghi,et al.  Stable alluvial channel design using evolutionary neural networks , 2018, Journal of Hydrology.

[20]  J. Chachi,et al.  A hybrid fuzzy regression model and its application in hydrology engineering , 2014, Appl. Soft Comput..

[21]  Babak Nadjar Araabi,et al.  Implementation of Emotional Controller for Interior Permanent Magnet Synchronous Motor Drive , 2006, Conference Record of the 2006 IEEE Industry Applications Conference Forty-First IAS Annual Meeting.

[22]  Saeed Reza Khodashenas.  Threshold gravel channels bank profile: a comparison among 13 models , 2016 .

[23]  Hassanzadeh Yousef,et al.  VALIDATION OF RIVER BANK PROFILES IN SAND-BED RIVERS , 2014 .

[24]  Bahram Gharabaghi,et al.  A methodological approach of predicting threshold channel bank profile by multi-objective evolutionary optimization of ANFIS , 2018 .

[25]  D. W. Knight,et al.  GEOMETRY OF SELF-FORMED STRAIGHT THRESHOLD CHANNELS IN UNIFORM MATERIAL. , 1998 .

[26]  Adnan Khashman,et al.  Credit risk evaluation using neural networks: Emotional versus conventional models , 2011, Appl. Soft Comput..

[27]  Adnan Khashman,et al.  Blood Cell Identification using Emotional Neural Networks , 2009, J. Inf. Sci. Eng..

[28]  Hossein Bonakdari,et al.  Developing an expert group method of data handling system for predicting the geometry of a stable channel with a gravel bed , 2017 .

[29]  S. Cao,et al.  Design for Hydraulic Geometry of Alluvial Channels , 1998 .

[30]  Gabriel Kaless,et al.  Regime theories in gravel‐bed rivers: models, controlling variables, and applications in disturbed Italian rivers , 2014 .

[31]  Amir Hossein Zaji,et al.  New radial basis function network method based on decision trees to predict flow variables in a curved channel , 2017, Neural Computing and Applications.

[32]  P. Diplas.  Characteristics of Self‐Formed Straight Channels , 1990 .

[33]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[34]  Gregorio G. Vigilar,et al.  Stable Channels with Mobile Bed: Model Verification and Graphical Solution , 1998 .

[35]  H. Bonakdari,et al.  No-deposition Sediment Transport in Sewers Using of Gene Expression Programming , 2017 .

[36]  G. Parker,et al.  Stable width and depth of straight gravel rivers with heterogeneous bed materials , 1988 .

[37]  R. Hey,et al.  Regime equations for natural meandering cobble and gravel bed rivers , 2011 .

[38]  James E. Pizzuto,et al.  Numerical simulation of gravel river widening , 1990 .

[39]  Renquan Lu,et al.  Adaptive Neural Network Tracking Control for Robotic Manipulators With Dead Zone , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[40]  Vahid Nourani,et al.  An Emotional ANN (EANN) approach to modeling rainfall-runoff process , 2017 .

[41]  Gregorio G. Vigilar,et al.  Stable Channels with Mobile Bed: Formulation and Numerical Solution , 1997 .

[42]  L. Pessoa.  On the relationship between emotion and cognition , 2008, Nature Reviews Neuroscience.

[43]  Amir Hossein Gandomi,et al.  A new predictive model for compressive strength of HPC using gene expression programming , 2012, Adv. Eng. Softw..

[44]  Hongyi Li,et al.  Adaptive finite-time tracking control of full state constrained nonlinear systems with dead-zone , 2019, Autom..

[45]  Vijay P. Singh,et al.  Hydraulic Geometry Relations for Stable Channel Design , 2010 .

[46]  Hossein Bonakdari,et al.  Design of an adaptive neuro-fuzzy computing technique for predicting flow variables in a 90° sharp bend , 2017 .

[47]  Ehsan Lotfi,et al.  BRAIN EMOTIONAL LEARNING-BASED PATTERN RECOGNIZER , 2013, Cybern. Syst..

[48]  J. Morén,et al.  A computational model of emotional learning in the amygdala. , 2000 .

[49]  Amir Hossein Gandomi,et al.  Design equations for prediction of pressuremeter soil deformation moduli utilizing expression programming systems , 2012, Neural Computing and Applications.

[50]  Syunsuke Ikeda,et al.  Self-Formed Straight Channels in Sandy Beds , 1981 .

[51]  Sarah Frances. Ackert.  A study of different methods for determination of regime channel geometry with application to streams in southwestern Ontario. , 2000 .

[52]  Ehsan Lotfi,et al.  A winner-take-all approach to emotional neural networks with universal approximation property , 2015, Inf. Sci..

[53]  Ahmed Bilal,et al.  Evaluation and Comparison of Extremal Hypothesis-Based Regime Methods , 2018 .

[54]  K. Babaeyan-Koopaei,et al.  A study of straight stable channels and their interactions with bridge structures , 1996 .

[55]  P. Julien,et al.  Downstream Hydraulic Geometry of Alluvial Channels , 2006 .

[56]  P. Armitage,et al.  Mid-P confidence intervals: a brief review , 1995 .

[57]  Florentino Fernández Riverola,et al.  A hybrid artificial intelligence model for river flow forecasting , 2013, Appl. Soft Comput..

[58]  Bahram Gharabaghi,et al.  Uncertainty analysis of intelligent model of hybrid genetic algorithm and particle swarm optimization with ANFIS to predict threshold bank profile shape based on digital laser approach sensing , 2018, Measurement.

[59]  Cândida Ferreira,et al.  Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence , 2014, Studies in Computational Intelligence.

[60]  Bahram Gharabaghi,et al.  Reservoir water level forecasting using group method of data handling , 2018, Acta Geophysica.

[61]  Gary Parker,et al.  A new vectorial bedload formulation and its application to the time evolution of straight river channels , 1994, Journal of Fluid Mechanics.

[62]  Amir Hossein Zaji,et al.  Improving the performance of multi-layer perceptron and radial basis function models with a decision tree model to predict flow variables in a sharp 90° bend , 2016, Appl. Soft Comput..

[63]  Jose Manuel Ramirez-Leon.  An investigation of the ability of a numerical model to predict the cross sectional shape of an alluvial channel , 2015 .

[64]  Hossein Bonakdari,et al.  Evaluation of artificial neural network model and statistical analysis relationships to predict the stable channel width , 2016 .

[65]  Yueying Wang,et al.  A sliding mode approach to stabilization of nonlinear Markovian jump singularly perturbed systems , 2018, Autom..

[66]  S. M. Bateni,et al.  Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS) , 2015 .

[67]  Bahram Gharabaghi,et al.  Reliable method of determining stable threshold channel shape using experimental and gene expression programming techniques , 2019, Neural Computing and Applications.