Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models.

The functional life of a dam is often determined by the rate of sediment delivery to its reservoir. Therefore, an accurate estimate of the sediment load in rivers with dams is essential for designing and predicting a dam's useful lifespan. The most credible method is direct measurements of sediment input, but this can be very costly and it cannot always be implemented at all gauging stations. In this study, we tested various regression models to estimate suspended sediment load (SSL) at two gauging stations on the Çoruh River in Turkey, including artificial bee colony (ABC), teaching-learning-based optimization algorithm (TLBO), and multivariate adaptive regression splines (MARS). These models were also compared with one another and with classical regression analyses (CRA). Streamflow values and previously collected data of SSL were used as model inputs with predicted SSL data as output. Two different training and testing dataset configurations were used to reinforce the model accuracy. For the MARS method, the root mean square error value was found to range between 35% and 39% for the test two gauging stations, which was lower than errors for other models. Error values were even lower (7% to 15%) using another dataset. Our results indicate that simultaneous measurements of streamflow with SSL provide the most effective parameter for obtaining accurate predictive models and that MARS is the most accurate model for predicting SSL.

[1]  Abbas Parsaie,et al.  Applications of soft computing techniques for prediction of energy dissipation on stepped spillways , 2016, Neural Computing and Applications.

[2]  Samia Nefti-Meziani,et al.  A Comprehensive Review of Swarm Optimization Algorithms , 2015, PloS one.

[3]  Abbas Parsaie,et al.  Prediction of energy dissipation on the stepped spillway using the multivariate adaptive regression splines , 2016 .

[4]  J. Friedman.  Multivariate adaptive regression splines , 1990 .

[5]  Tayfun Dede,et al.  Optimum design of grillage structures to LRFD-AISC with teaching-learning based optimization , 2013 .

[6]  Siti Fatin Mohd Razali,et al.  The Application of Artificial Bee Colony and Gravitational Search Algorithm in Reservoir Optimization , 2016, Water Resources Management.

[7]  Ashish Pandey,et al.  Modelling of runoff and sediment yield using ANN, LS-SVR, REPTree and M5 models , 2017 .

[8]  Sajjad Ahmad,et al.  Suspended sediment load prediction of river systems: An artificial neural network approach , 2011 .

[9]  Vahid Nourani,et al.  Daily and monthly suspended sediment load predictions using wavelet based artificial intelligence approaches , 2015, Journal of Mountain Science.

[10]  Mehmet Berkun,et al.  Hydroelectric potential and environmental effects of multidam hydropower projects in Turkey , 2010 .

[11]  Murat İhsan Kömürcü,et al.  Prediction of berm geometry using a set of laboratory tests combined with teaching–learning-based optimization and artificial bee colony algorithms , 2014 .

[12]  B. Remini,et al.  Impact of suspended sediment load on the silting of SMBA reservoir (Algeria) , 2014, Environmental Earth Sciences.

[13]  Ozgur Kisi,et al.  Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data , 2009 .

[14]  Ozgur Kisi,et al.  Methods to improve the neural network performance in suspended sediment estimation , 2006 .

[15]  Hikmet Kerem Cigizoglu,et al.  Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data , 2007, Environ. Model. Softw..

[16]  Murat Kankal,et al.  Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey , 2014 .

[17]  Pijush Samui,et al.  Determination of ultimate capacity of driven piles in cohesionless soil: A Multivariate Adaptive Regression Spline approach , 2012 .

[18]  Mehmet Fatih Tasgetiren,et al.  A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem , 2011, Inf. Sci..

[19]  Taher Rajaee,et al.  Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers. , 2011, The Science of the total environment.

[20]  Ashish Pandey,et al.  Daily suspended sediment simulation using machine learning approach , 2016 .

[21]  Manish Kumar Goyal,et al.  Modeling of Sediment Yield Prediction Using M5 Model Tree Algorithm and Wavelet Regression , 2014, Water Resources Management.

[22]  O. Kisi,et al.  Neural networks with artificial bee colony algorithm for modeling daily reference evapotranspiration , 2011, Irrigation Science.

[23]  Sharad K. Jain,et al.  Development of Integrated Sediment Rating Curves Using ANNs , 2001 .

[24]  R. Anitha,et al.  MARSpline model for lead seven-day maximum and minimum air temperature prediction in Chennai, India , 2014, Journal of Earth System Science.

[25]  Zohre Sadat Pourtaghi,et al.  GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran , 2016, Environmental Earth Sciences.

[26]  M. Janga Reddy,et al.  Use of Model Tree and Gene Expression Programming to Predict the Suspended Sediment Load in Rivers , 2009 .

[27]  Samad Emamgholizadeh,et al.  A comparison of artificial intelligence models for the estimation of daily suspended sediment load: a case study on the Telar and Kasilian rivers in Iran , 2019 .

[28]  Yusuf Ayvaz,et al.  Combined size and shape optimization of structures with a new meta-heuristic algorithm , 2015, Appl. Soft Comput..

[29]  M. Demirci,et al.  Forecasting of Suspended Sediment in Rivers Using Artificial Neural Networks Approach , 2017 .

[30]  Amir Hamzeh Haghiabi,et al.  Prediction of River Pipeline Scour Depth Using Multivariate Adaptive Regression Splines , 2017 .

[31]  Ajith Abraham,et al.  Elitist Teaching Learning Opposition based algorithm for global optimization , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[32]  Mustafa Demirci,et al.  Prediction of suspended sediment in river using fuzzy logic and multilinear regression approaches , 2012, Neural Computing and Applications.

[33]  Anthony T. C. Goh,et al.  Multivariate adaptive regression splines and neural network models for prediction of pile drivability , 2016 .

[34]  Kwok-Wing Chau,et al.  A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States , 2015, Environmental Monitoring and Assessment.

[35]  Wei-Chang Yeh,et al.  Artificial bee colony algorithm-neural networks for S-system models of biochemical networks approximation , 2010, Neural Computing and Applications.

[36]  Jalal Chachi,et al.  Suspended load estimation using L1-fuzzy regression, L2-fuzzy regression and MARS-fuzzy regression models , 2016 .

[37]  Dervis Karaboga,et al.  Artificial bee colony programming for symbolic regression , 2012, Inf. Sci..

[38]  Vivek K. Patel,et al.  A multi-objective improved teaching-learning based optimization algorithm (MO-ITLBO) , 2016, Inf. Sci..

[39]  D. Antanasijević,et al.  Prediction of clearing temperatures of bent-core liquid crystals using decision trees and multivariate adaptive regression splines , 2016 .

[40]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[41]  Maurice R Puyau,et al.  Multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents. , 2010, Journal of applied physiology.

[42]  Taher Niknam,et al.  A new multi objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems , 2012, Eng. Appl. Artif. Intell..

[43]  Tayfun Dede,et al.  Modeling stream dissolved oxygen concentration using teaching–learning based optimization algorithm , 2015, Environmental Earth Sciences.

[44]  Meral Buyukyildiz,et al.  An Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System, Support Vector Machine and Artificial Neural Network Models , 2017, Water Resources Management.

[45]  O. Kisi,et al.  Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution , 2016 .

[46]  Kwok-wing Chau,et al.  A Hybrid Double Feedforward Neural Network for Suspended Sediment Load Estimation , 2016, Water Resources Management.

[47]  H. K. Cigizoglu,et al.  ESTIMATION AND FORECASTING OF DAILY SUSPENDED SEDIMENT DATA BY MULTI-LAYER PERCEPTRONS , 2004 .

[48]  Edward A. McBean,et al.  Uncertainty in Suspended Sediment Transport Curves , 1988 .

[49]  Yuming Zhou,et al.  Predicting object-oriented software maintainability using multivariate adaptive regression splines , 2007, J. Syst. Softw..

[50]  Shiv O. Prasher,et al.  Modeling runoff from middle Himalayan watersheds employing artificial intelligence techniques , 2006 .

[51]  Dantong Ouyang,et al.  An artificial bee colony approach for clustering , 2010, Expert Syst. Appl..

[52]  R. Venkata Rao,et al.  Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms , 2012 .

[53]  Abbas Parsaie,et al.  Improving Modelling of Discharge Coefficient of Triangular Labyrinth Lateral Weirs Using SVM, GMDH and MARS Techniques , 2017 .

[54]  Ozgur Kisi,et al.  Modeling discharge–sediment relationship using neural networks with artificial bee colony algorithm , 2012 .

[55]  K. Bashar,et al.  Nile Basin Reservoir Sedimentation Prediction and Mitigation , 2010 .

[56]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[57]  Tayfun Dede,et al.  Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm , 2014 .

[58]  Vahid Nourani,et al.  Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models. , 2009, The Science of the total environment.

[59]  Abdüsselam Altunkaynak,et al.  Sediment load prediction by genetic algorithms , 2009, Adv. Eng. Softw..

[60]  Anima Naik,et al.  Data Clustering Based on Teaching-Learning-Based Optimization , 2011, SEMCCO.

[61]  Pijush Samui,et al.  A Multivariate Adaptive Regression Spline Approach for Prediction of Maximum Shear Modulus and Minimum Damping Ratio , 2012 .

[62]  Prabhata K. Swamee,et al.  Modeling of Suspended Sediment Concentration at Kasol in India Using ANN, Fuzzy Logic, and Decision Tree Algorithms , 2012 .

[63]  Murat Kankal,et al.  Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey , 2017, Neural Computing and Applications.

[64]  Amir Ahmad Dehghani,et al.  Genetic Algorithm Model for the Relation Between Flow Discharge and Suspended Sediment Load ( Gorgan River in Iran ) , 2011 .

[65]  R. Venkata Rao,et al.  Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems , 2012, Inf. Sci..

[66]  Özgür Kisi,et al.  River suspended sediment estimation by climatic variables implication: Comparative study among soft computing techniques , 2012, Comput. Geosci..

[67]  M. Çimen,et al.  Estimation of daily suspended sediments using support vector machines , 2008 .

[68]  Nurhan Karaboga,et al.  A new design method based on artificial bee colony algorithm for digital IIR filters , 2009, J. Frankl. Inst..

[69]  A. Ahmadi,et al.  Daily suspended sediment load prediction using artificial neural networks and support vector machines , 2013 .

[70]  Vedat Toğan,et al.  Design of planar steel frames using Teaching–Learning Based Optimization , 2012 .

[71]  Özgür Kisi,et al.  Adaptive neuro-fuzzy computing technique for suspended sediment estimation , 2009, Adv. Eng. Softw..

[72]  Sevinc D. Ozkul,et al.  Predicting Suspended Sediment Loads and Missing Data for Gediz River, Turkey , 2009 .

[73]  Murat Kankal,et al.  Assessment of hydropower and multi-dam power projects in Turkey , 2014 .

[74]  Vahid Moosavi,et al.  Estimation of suspended sediment load using regression trees and model trees approaches (Case study: Hyderabad drainage basin in Iran) , 2017 .

[75]  R. V. Rao,et al.  Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems , 2012 .