What Is the Potential of Integrating Phase Space Reconstruction with SVM-FFA Data-Intelligence Model? Application of Rainfall Forecasting over Regional Scale

Rainfall modeling is one of the major component process in the meteorological engineering. Hence, exploring an advance and reliable intelligent model for its forecasting is essential for water resources engineering. In this current research, novel integrative intelligence model coupled with phase space reconstruction is proposed to forecast monthly rainfall in Chhattisgarh State, India. The proposed model is a hybridization of support vector machine (SVM) model with firefly optimization algorithm (FFA). The modeling is undertaken based on three stages starting with configuring the delay time and embedding dimension using mutual information and false nearest neighbors to determine the input matrix of the forecasting model. In the second stage, the firefly optimizer is employed to tune the SVM model. Finally, the hybrid model is conducted to forecast the monthly time scale rainfall time series. Monthly time scale rainfall data for sixteen raingauge stations over a century (1901–2002) are utilized and tested. A validation of the capacity of the suggested model is carried out by comparing the accuracy results with classical SVM and hybrid SVM-FFA “without mutual information analysis” models. The three predictive models are trained using 75% of available data set and tested the remaining 25% dataset. The model’s results were statistically verified using mean absolute error and best-good-fitness measurements in addition to Taylor diagram visualization. In conclusion, the proposed model was significantly improved the forecasting accuracy of the modeling. Also, it was exhibited a very robust intelligent model that can be applied for the Indian regional zone for monthly rainfall forecasting.

[1]  Willis Gwenzi,et al.  Hydrological Impacts of Urbanization and Urban Roof Water Harvesting in Water-limited Catchments: A Review , 2014, Environmental Processes.

[2]  Vandana,et al.  Survey of Nearest Neighbor Techniques , 2010, ArXiv.

[3]  V. Jothiprakash,et al.  Streamflow variability and classification using false nearest neighbor method , 2015 .

[4]  M. Katz.  Validation of models , 2006 .

[5]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[6]  Zhongyi Hu,et al.  A PSO and pattern search based memetic algorithm for SVMs parameters optimization , 2013, Neurocomputing.

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

[8]  Mohammad Ali Ghorbani,et al.  A novel hybrid neural network based on phase space reconstruction technique for daily river flow prediction , 2018, Soft Comput..

[9]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[10]  V. Nourani,et al.  A hybrid support vector regression–firefly model for monthly rainfall forecasting , 2018, International Journal of Environmental Science and Technology.

[11]  Shervin Motamedi,et al.  A hybrid SVM-FFA method for prediction of monthly mean global solar radiation , 2015, Theoretical and Applied Climatology.

[12]  Shian-Chang Huang,et al.  WAVELET-BASED RELEVANCE VECTOR REGRESSION MODEL COUPLED WITH PHASE SPACE RECONSTRUCTION FOR EXCHANGE RATE FORECASTING , 2011 .

[13]  R Hegger,et al.  Improved false nearest neighbor method to detect determinism in time series data. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[14]  Bin Wang,et al.  Methods for uncertainty assessment of climate models and model predictions over East Asia , 2014 .

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

[16]  Li Hui.  Wind Power Short-term Forecasting Based on Empirical Mode Decomposition and Chaotic Phase Space Reconstruction , 2012 .

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

[18]  Ashish Pandey,et al.  Evaluation of TRMM multi-satellite precipitation analysis (TMPA) against terrestrial measurement over a humid sub-tropical basin, India , 2017, Theoretical and Applied Climatology.

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

[20]  O. Kisi,et al.  SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment , 2012 .

[21]  Peng Tian,et al.  Variations in extreme precipitation on the Loess Plateau using a high-resolution dataset and their linkages with atmospheric circulation indices , 2018, Theoretical and Applied Climatology.

[22]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[23]  T. Chai,et al.  Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature , 2014 .

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

[25]  Naser El-Sheimy,et al.  Autonomous smartphone-based WiFi positioning system by using access points localization and crowdsourcing , 2015, Pervasive Mob. Comput..

[26]  K. Taylor.  Summarizing multiple aspects of model performance in a single diagram , 2001 .

[27]  Babak Mohammadi,et al.  Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran , 2018, Theoretical and Applied Climatology.

[28]  Zaher Mundher Yaseen,et al.  Artificial intelligence based models for stream-flow forecasting: 2000-2015 , 2015 .

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

[30]  Xin Yao,et al.  Many-Objective Evolutionary Algorithms , 2015, ACM Comput. Surv..

[31]  Kasım Koçak,et al.  SVR-based prediction of evaporation combined with chaotic approach , 2014 .

[32]  Paresh Chandra Deka,et al.  Support vector machine applications in the field of hydrology: A review , 2014, Appl. Soft Comput..

[33]  Satchidananda Dehuri,et al.  Evolutionary Algorithms for Multi-Criterion Optimization: A Survey , 2004 .

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

[35]  R. Deo,et al.  Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq , 2016 .

[36]  H. Abarbanel,et al.  Prediction in chaotic nonlinear systems: Methods for time series with broadband Fourier spectra. , 1990, Physical review. A, Atomic, molecular, and optical physics.

[37]  Ozgur Kisi,et al.  Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review , 2014 .

[38]  Shahaboddin Shamshirband,et al.  The use of SVM-FFA in estimating fatigue life of polyethylene terephthalate modified asphalt mixtures , 2016 .

[39]  Dalibor Petković,et al.  Long-Term Precipitation Analysis and Estimation of Precipitation Concentration Index Using Three Support Vector Machine Methods , 2016 .

[40]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[41]  Magnus Persson,et al.  Monthly runoff prediction using phase space reconstruction , 2001 .

[42]  Bellie Sivakumar,et al.  A phase-space reconstruction approach to prediction of suspended sediment concentration in rivers , 2002 .

[43]  C. Willmott.  ON THE VALIDATION OF MODELS , 1981 .

[44]  Issarachai Ngamroo,et al.  A heuristic training-based least squares support vector machines for power system stabilization by SMES , 2011, Expert Syst. Appl..

[45]  P. Coulibaly,et al.  Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting , 2012 .

[46]  Long Jin,et al.  A Hybrid Support Vector Regression Approach for Rainfall Forecasting Using Particle Swarm Optimization and Projection Pursuit Technology , 2010, Int. J. Comput. Intell. Appl..

[47]  H. Nagy,et al.  Prediction of Sediment Load Concentration in Rivers using Artificial Neural Network Model , 2002 .

[48]  Yang Liu,et al.  Collaborative Security , 2015, ACM Comput. Surv..