A Multi-Layer Perceptron (MLP)-Fire Fly Algorithm (FFA)-based model for sediment prediction

The prediction of river sediment load is an essential issue in water resource engineering problems. In this study, artificial neural network employed in order to estimate the daily sediment load on rivers. Two different algorithms, multi-layer perceptron (MLP) and hybrid MLP-FFA (MLP integrated with the FFA) were used for this purpose in the Lake Mahabad, Iran. For this purpose, nine different scenarios are considered as inputs of the models. Performance of selected models was evaluated on basis of performance criterion namely root mean square error (RMSE), mean absolute error (MAE), coefficient of determination ( R 2 ) for choosing best fit model. The results indicated that the new hybrid model MLP-FFA is successful in estimating sediment load with high accuracy as compared with its alternatives with RMSE = 2018 ton/day, MAE = 1698 and R 2  = 0.95, which were much lower than those of MLP-based model with RMSE = 3044 ton/day, MAE = 2481 and R 2  = 0.90. The results of the present study confirmed the suitability of proposed methodology for precise modeling of suspended sediment load.

[1]  Ozgur Kisi,et al.  Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models , 2021, Soft Computing.

[2]  Bahram Saghafian,et al.  Application of the WEPP model to determine sources of run‐off and sediment in a forested watershed , 2015 .

[3]  M. Ghorbani,et al.  Relative importance of parameters affecting wind speed prediction using artificial neural networks , 2013, Theoretical and Applied Climatology.

[4]  A. Yadav,et al.  Prediction of suspended sediment yield by artificial neural network and traditional mathematical model in Mahanadi river basin, India , 2018, Sustainable Water Resources Management.

[5]  Lan Zhang,et al.  A Regression-Based Prediction Model of Suspended Sediment Yield in the Cuyahoga River in Ohio Using Historical Satellite Images and Precipitation Data , 2020, Water.

[6]  Shahaboddin Shamshirband,et al.  River flow prediction using hybrid PSOGSA algorithm based on feed-forward neural network , 2018, Soft Comput..

[7]  Zhaolong Ning,et al.  A collective filtering based content transmission scheme in edge of vehicles , 2020, Inf. Sci..

[8]  Sajjad Ahmad,et al.  Estimation of Clark’s Instantaneous Unit Hydrograph Parameters and Development of Direct Surface Runoff Hydrograph , 2009 .

[9]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

[10]  Zaher Mundher Yaseen,et al.  Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review , 2017, Theoretical and Applied Climatology.

[11]  A. Thompson,et al.  Estimating Sediment Delivery Ratios for Grassed Waterways using WEPP , 2017 .

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

[13]  Y. Niazi,et al.  Soil erosion and sediment yield modeling using RS and GIS techniques: a case study, Iran , 2012, Arabian Journal of Geosciences.

[14]  K. P. Sudheer,et al.  Estimating Actual Evapotranspiration from Limited Climatic Data Using Neural Computing Technique , 2003 .

[15]  Ozgur Kisi,et al.  Wavelet regression model as an alternative to neural networks for monthly streamflow forecasting , 2009 .

[16]  S. Dutta,et al.  Application of SWAT model for predicting soil erosion and sediment yield , 2018, Sustainable Water Resources Management.

[17]  B. Bobée,et al.  Artificial neural network modeling of water table depth fluctuations , 2001 .

[18]  L. Mays,et al.  OPTIMIZATION OF MULTIPLE RESERVOIR NETWORKS FOR SEDIMENTATION CONTROL , 2000 .

[19]  Ozgur Kisi,et al.  Streamflow Forecasting Using Different Artificial Neural Network Algorithms , 2007 .

[20]  Mohd Yawar Ali Khan,et al.  Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga, Ganges Basin, India , 2019, International Journal of Sediment Research.

[21]  Kasra Mohammadi,et al.  A support vector machine–firefly algorithm-based model for global solar radiation prediction , 2015 .

[22]  Mir Jafar Sadegh Safari,et al.  Implementation of hybrid particle swarm optimization-differential evolution algorithms coupled with multi-layer perceptron for suspended sediment load estimation , 2020 .

[23]  Y. Bissonnais,et al.  Prediction of sediment load by sediment rating curve and neural network (ANN) in El Kebir catchment, Algeria , 2013, Journal of Earth System Science.

[24]  S. B. Awulachew,et al.  SWAT based runoff and sediment yield modelling: a case study of the Gumera watershed in the Blue Nile basin. , 2010 .

[25]  Vitaly Schetinin.  A Learning Algorithm for Evolving Cascade Neural Networks , 2004, Neural Processing Letters.

[26]  Salim Heddam,et al.  Simultaneous modelling and forecasting of hourly dissolved oxygen concentration (DO) using radial basis function neural network (RBFNN) based approach: a case study from the Klamath River, Oregon, USA , 2016, Modeling Earth Systems and Environment.

[27]  Ozgur Kisi,et al.  River Suspended Sediment Load Prediction: Application of ANN and Wavelet Conjunction Model , 2011 .

[28]  A. K. Lohani,et al.  Deriving stage–discharge–sediment concentration relationships using fuzzy logic , 2007 .

[29]  Mohammad Ali Ghorbani,et al.  New Approach for Sediment Yield Forecasting with a Two-Phase Feedforward Neuron Network-Particle Swarm Optimization Model Integrated with the Gravitational Search Algorithm , 2019, Water Resources Management.

[30]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[31]  K. Oost,et al.  An assessment of the global impact of 21st century land use change on soil erosion , 2017, Nature Communications.

[32]  A. Kalra,et al.  Estimating soil moisture using remote sensing data: A machine learning approach , 2010 .

[33]  Donald K. McCool,et al.  Modeling the impacts of no-till practice on soil erosion and sediment yield with RUSLE, SEDD, and ArcView GIS , 2006 .

[34]  Using the Remote Sensing and GIS Technology for Erosion Risk Mapping of Kartalkaya Dam Watershed in Kahramanmaras, Turkey , 2008, Sensors.

[35]  Jürgen Schmidhuber,et al.  Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks , 2006, ICML.

[36]  M. Minaei,et al.  Evaluation of WEPP and EPM for improved predictions of soil erosion in mountainous watersheds: A case study of Kangir River basin, Iran , 2020, Modeling Earth Systems and Environment.

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

[38]  Vahid Nourani,et al.  Conjunction of radial basis function interpolator and artificial intelligence models for time-space modeling of contaminant transport in porous media , 2017 .

[39]  Souad Riad,et al.  Rainfall-runoff model usingan artificial neural network approach , 2004, Math. Comput. Model..

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

[41]  G. Joshi,et al.  Modeling of Rainfall-Runoff Correlations Using Artificial Neural Network-A Case Study of Dharoi Watershed of a Sabarmati River Basin, India , 2017 .

[42]  Shahaboddin Shamshirband,et al.  Prediction of Daily Dewpoint Temperature Using a Model Combining the Support Vector Machine with Firefly Algorithm , 2016 .

[43]  F. Woldemeskel,et al.  Complex networks, community structure, and catchment classification in a large-scale river basin , 2017 .

[44]  Determination of Reservoir Capacity UsingLinear Programming , 2015 .

[45]  N. Null.  Artificial Neural Networks in Hydrology. I: Preliminary Concepts , 2000 .

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

[47]  Vijay P. Singh,et al.  Two-Dimensional Kinematic Wave Model of Overland-Flow , 2004 .

[48]  Slawomir Zak,et al.  Firefly Algorithm for Continuous Constrained Optimization Tasks , 2009, ICCCI.

[49]  Shervin Motamedi,et al.  Soft computing approaches for forecasting reference evapotranspiration , 2015, Comput. Electron. Agric..

[50]  V. Singh,et al.  Kinematic Wave Theory for Transient Bed Sediment Waves in Alluvial Rivers , 2008 .

[51]  Slavisa Trajkovic,et al.  Temperature-based approaches for estimating reference evapotranspiration , 2005 .

[52]  Hui Jiang,et al.  Sediment Yield Modeling Using SWAT Model: Case of Changjiang River Basin , 2019, IOP Conference Series: Earth and Environmental Science.

[53]  O. Kisi,et al.  A genetic programming approach to suspended sediment modelling , 2008 .

[54]  K. Khosravi,et al.  Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction , 2020, Environmental Science and Pollution Research.

[55]  V. Singh,et al.  Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction , 2020, Water Resources Management.

[56]  Bijaya K. Panigrahi,et al.  A Support Vector Machine-Firefly Algorithm based forecasting model to determine malaria transmission , 2014, Neurocomputing.

[57]  Hamid Reza Pourghasemi,et al.  A comparative study between dynamic and soft computing models for sediment forecasting , 2021, Soft Computing.

[58]  Jan Adamowski,et al.  Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction , 2016 .

[59]  Celso Augusto Guimarães Santos,et al.  Streamflow Prediction Based on Artificial Intelligence Techniques , 2021, Iranian Journal of Science and Technology, Transactions of Civil Engineering.

[60]  Kuolin Hsu,et al.  Artificial Neural Network Modeling of the Rainfall‐Runoff Process , 1995 .

[61]  Shiv O. Prasher,et al.  Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data , 2012 .

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

[63]  S. G. Meshram,et al.  Predicting reservoir volume reduction using artificial neural network , 2020, Arabian Journal of Geosciences.

[64]  Christina Magill,et al.  Building vulnerability to hydro-geomorphic hazards: Estimating damage probability from qualitative vulnerability assessment using logistic regression , 2016 .

[65]  Mohammad Ali Ghorbani,et al.  Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point , 2017 .

[66]  K. P. Sudheer,et al.  Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions , 2010, Environ. Model. Softw..

[67]  W. Schneider.  On basic equations and kinematic-wave theory of separation processes in suspensions with gravity, centrifugal and Coriolis forces , 2018 .

[68]  P. D. Porey,et al.  Estimation of sediment yield using SWAT model for Upper Tapi basin , 2014 .