A hybrid wavelet-support vector machine model for prediction of Lake water level fluctuations using hydro-meteorological data

Abstract Prediction of Lake water level fluctuation is immensely important to maintain the ecological conditions of the lake as well as for the development and management of water resources. Loktak Lake, a Ramsar convention recognised wetland have a unique ecosystem and is known for its floating vegetated islands. Fluctuation of water level in Loktak Lake primarily due to hydropower and agriculture have impacted the lake ecosystem significantly. The study employed a hybrid wavelet-support vector machine (WA-SVM) model to predict the daily lake water fluctuation. Daily lake water level along with other hydro-meteorological data have been used as input to predict the lake water level up to 20 days ahead. Various combinations of input data have been experimented to obtain the best model structure which is later extended to predict the lake water level. The predicted lake water are found to be close to observed values.

[1]  Basant Yadav,et al.  Estimation of in-situ bioremediation system cost using a hybrid Extreme Learning Machine (ELM)-particle swarm optimization approach , 2016 .

[2]  Bhoop Singh,et al.  Artificial neural network model as a potential alternative for groundwater salinity forecasting , 2011 .

[3]  Amit K. Verma,et al.  Prediction of water quality from simple field parameters , 2013, Environmental Earth Sciences.

[4]  Chandranath Chatterjee,et al.  Development of an accurate and reliable hourly flood forecasting model using wavelet–bootstrap–ANN (WBANN) hybrid approach , 2010 .

[5]  Huan Wang,et al.  A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida , 2015, Water Resources Management.

[6]  Ozgur Kisi,et al.  Comparison of two different data-driven techniques in modeling lake level fluctuations in Turkey , 2009 .

[7]  Dimitri Solomatine,et al.  Groundwater Model Approximation with Artificial Neural Network for Selecting Optimum Pumping Strategy for Plume Removal , 2001 .

[8]  Shahaboddin Shamshirband,et al.  Prediction of Water-Level in the Urmia Lake Using the Extreme Learning Machine Approach , 2016, Water Resources Management.

[9]  Yunjung Hyun,et al.  Time series modeling for evaluation of groundwater discharge rates into an urban subway system , 2005 .

[10]  C. W. Chan,et al.  Modelling of river discharges and rainfall using radial basis function networks based on support vector regression , 2003, Int. J. Syst. Sci..

[11]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[12]  Paulin Coulibaly,et al.  Groundwater level forecasting using artificial neural networks , 2005 .

[13]  O. Kisi,et al.  Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model , 2010 .

[14]  Ozgur Kisi,et al.  A wavelet-support vector machine conjunction model for monthly streamflow forecasting , 2011 .

[15]  R. R. Sethi,et al.  Prediction of water table depth in a hard rock basin by using artificial neural network , 2010 .

[16]  Kourosh Mohammadi,et al.  Groundwater Table Estimation Using MODFLOW and Artificial Neural Networks , 2009 .

[17]  S. Liong,et al.  EC-SVM approach for real-time hydrologic forecasting , 2004 .

[18]  J. R. French,et al.  Modelling water-level options for ecosystem services and assessment of climate change: Loktak Lake, northeast India , 2011 .

[19]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[20]  Elahe Fallah-Mehdipour,et al.  Prediction and simulation of monthly groundwater levels by genetic programming , 2013 .

[21]  Linda See,et al.  Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning , 2006 .

[22]  Shakeel Ahmed,et al.  Comparison of FFNN and ANFIS models for estimating groundwater level , 2011 .

[23]  J. Adamowski,et al.  A wavelet neural network conjunction model for groundwater level forecasting , 2011 .

[24]  X. Wen,et al.  A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region , 2014 .

[25]  K. Lee,et al.  A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer , 2011 .

[26]  MohammadSajjad Khan,et al.  Application of Support Vector Machine in Lake Water Level Prediction , 2006 .

[27]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[28]  O. Kisi.  Neural Networks and Wavelet Conjunction Model for Intermittent Streamflow Forecasting , 2009 .

[29]  Shie-Yui Liong,et al.  Rainfall and runoff forecasting with SSA-SVM approach , 2001 .

[30]  Jan Adamowski,et al.  Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. , 2010 .

[31]  Ashwani Kumar,et al.  Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi–Surua Inter-basin of Odisha, India , 2013 .

[32]  K. P. Sudheer,et al.  Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India , 2010 .

[33]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[34]  Emery Coppola,et al.  Comparative Study of SVMs and ANNs in Aquifer Water Level Prediction , 2010, J. Comput. Civ. Eng..

[35]  Ming J. Zuo,et al.  Support vector machine based data processing algorithm for wear degree classification of slurry pump systems , 2010 .

[36]  S. S. Nair,et al.  Groundwater level forecasting using Artificial Neural Network , 2016 .

[37]  Samad Emamgholizadeh,et al.  Prediction the Groundwater Level of Bastam Plain (Iran) by Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) , 2014, Water Resources Management.

[38]  Hamid R. Safavi,et al.  Conjunctive Use of Surface Water and Groundwater: Application of Support Vector Machines (SVMs) and Genetic Algorithms , 2013, Water Resources Management.

[39]  Bijaya K. Panigrahi,et al.  An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India , 2014, Neurocomputing.

[40]  O. Kisi.  River flow forecasting and estimation using different artificial neural network techniques , 2008 .

[41]  Ozgur Kisi,et al.  Estimation of Daily Suspended Sediment Load by Using Wavelet Conjunction Models , 2012 .