Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts

Abstract Persistent risks of extreme weather events including droughts and floods due to climate change require precise and timely rainfall forecasting. Yet, the naturally occurring non-stationarity entrenched within the rainfall time series lowers the model performances and is an ongoing research endeavour for practicing hydrologists and drought-risk evaluators. In this paper, an attempt is made to resolve the non-stationarity challenges faced by rainfall forecasting models via a complete ensemble empirical mode decomposition (CEEMD) combined with Random Forest (RF) and Kernel Ridge Regression (KRR) algorithms in designing a hybrid CEEMD-RF-KRR model in forecasting rainfall at Gilgit, Muzaffarabad, and Parachinar in Pakistan at monthly time scale. The rainfall time-series data are simultaneously factorized into respective intrinsic mode functions (IMFs) and a residual element using CEEMD. Once the significant lags of each IMF and the residual are identified, both are forecasted using the RF algorithm. Finally, the KRR model is adopted where the forecasted IMFs and the residual components are combined to generate the final forecasted rainfall. The CEEMD-RF-KRR model shows the best performances at all three sites, in comparison to the comparative models, with maximum values of correlation coefficient (0.97–0.99), Willmott’s index (0.94–0.97), Nash-Sutcliffe coefficient (0.94–0.97) and Legates-McCabe’s index (0.74–0.81). Furthermore, the CEEMD-RF-KRR model generated the most accurate results for Gilgit station considering the Legate-McCabe’s index as base assessment criteria in addition to obtaining the lowest magnitudes of RMSE = 2.52 mm and MAE = 1.98 mm. The proposed hybrid CEEMD-RF-KRR model attained better rainfall forecasting accuracy which is imperative for agriculture, water resource management, and early drought/flooding warning systems.

[1]  J. Abbot,et al.  Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks , 2014 .

[2]  Amrit Pal Singh,et al.  Comparative Study of Firefly Algorithm and Particle Swarm Optimization for Noisy Non- Linear Optimization Problems , 2012 .

[3]  Shou-yu Chen,et al.  Improved annual rainfall-runoff forecasting using PSO-SVM model based on EEMD , 2013 .

[4]  P. McIntyre,et al.  Global threats to human water security and river biodiversity , 2010, Nature.

[5]  R. Deo,et al.  Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm , 2017 .

[6]  M. Poage.  Empirical Relationships Between Elevation and the Stable Isotope Composition of Precipitation and Surface Waters: Considerations for Studies of Paleoelevation Change , 2001 .

[7]  Yan Li,et al.  Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition , 2018, Geoderma.

[8]  Cort J. Willmott,et al.  On the Evaluation of Model Performance in Physical Geography , 1984 .

[9]  Julian Koch,et al.  Modelling of the shallow water table at high spatial resolution using random forests , 2019 .

[10]  José I. Barredo,et al.  Major flood disasters in Europe: 1950–2005 , 2007 .

[11]  Robert J. Abrahart,et al.  HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts , 2007, Environ. Model. Softw..

[12]  I. Moore,et al.  Digital terrain modelling: A review of hydrological, geomorphological, and biological applications , 1991 .

[13]  Jérôme Gilles,et al.  Empirical Wavelet Transform , 2013, IEEE Transactions on Signal Processing.

[14]  W. Collins,et al.  The Formulation and Atmospheric Simulation of the Community Atmosphere Model Version 3 (CAM3) , 2006 .

[15]  James Demmel,et al.  Accurate, Fast and Scalable Kernel Ridge Regression on Parallel and Distributed Systems , 2018, ICS.

[16]  Thong Nguyen-Huy,et al.  Copula-statistical precipitation forecasting model in Australia’s agro-ecological zones , 2017 .

[17]  Hayley J. Fowler,et al.  Using meteorological data to forecast seasonal runoff on the River Jhelum, Pakistan , 2008 .

[18]  A. Shamseldin.  Application of a neural network technique to rainfall-runoff modelling , 1997 .

[19]  Koichi Nakayama,et al.  A neural network-based local rainfall prediction system using meteorological data on the Internet: A case study using data from the Japan Meteorological Agency , 2017, Appl. Soft Comput..

[20]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[21]  Nitin K. Tripathi,et al.  An artificial neural network model for rainfall forecasting in Bangkok, Thailand , 2008 .

[22]  H. Oeschger,et al.  Correlation of 18O in precipitation with temperature and altitude , 1980, Nature.

[23]  M. Bauer.  The role of remote sensing in determining the distribution and yield of crops , 1975 .

[24]  Ashish Sharma,et al.  An application of artificial neural networks for rainfall forecasting , 2001 .

[25]  Q. Ouyang,et al.  Monthly Rainfall Forecasting Using EEMD-SVR Based on Phase-Space Reconstruction , 2016, Water Resources Management.

[26]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[27]  Yang Hong,et al.  Uncertainty analysis of bias from satellite rainfall estimates using copula method , 2014 .

[28]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[29]  W. Guo,et al.  A diagram for evaluating multiple aspects of model performance in simulating vector fields , 2016 .

[30]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[31]  D. Legates,et al.  Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation , 1999 .

[32]  Michalis E. Zervakis,et al.  Solar Radiation Time-Series Prediction Based on Empirical Mode Decomposition and Artificial Neural Networks , 2014, AIAI.

[33]  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 .

[34]  Ravinesh C. Deo,et al.  An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index , 2018, Atmospheric Research.

[35]  Seyoum Y. Gebremariam,et al.  A comprehensive approach to evaluating watershed models for predicting river flow regimes critical to downstream ecosystem services , 2014, Environ. Model. Softw..

[36]  Ruth Langridge,et al.  Access and Resilience: Analyzing the Construction of Social Resilience to the Threat of Water Scarcity , 2006 .

[37]  T. McMahon,et al.  El Nino/Southern Oscillation and Australian rainfall, streamflow and drought : Links and potential for forecasting , 1998 .

[38]  KashiwaoTomoaki,et al.  A neural network-based local rainfall prediction system using meteorological data on the Internet , 2017 .

[39]  D. A. Mooley,et al.  Large-Scale Droughts/Floods and Monsoon Circulation , 1980 .

[40]  Ravinesh C. Deo,et al.  Multi-stage hybridized online sequential extreme learning machine integrated with Markov Chain Monte Carlo copula-Bat algorithm for rainfall forecasting , 2018, Atmospheric Research.

[41]  Gwo-Fong Lin,et al.  Effective forecasting of hourly typhoon rainfall using support vector machines , 2009 .

[42]  J. Salas,et al.  Forecasting of short-term rainfall using ARMA models , 1993 .

[43]  Yang Qing,et al.  A Preliminary Analysis of the Relationship between Precipitation Variation Trends and Altitude in China , 2011 .

[44]  Norden E. Huang,et al.  On the time-varying trend in global-mean surface temperature , 2011 .

[45]  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.

[46]  Holger R. Maier,et al.  Future research challenges for incorporation of uncertainty in environmental and ecological decision-making , 2008 .

[47]  Ben Chie Yen.  Discussion and Closure: Criteria for Evaluation of Watershed Models , 1995 .

[48]  Ozgur Kisi,et al.  Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process , 2011 .

[49]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[50]  M. Romagnoli,et al.  Assessment of the SWAT model to simulate a watershed with limited available data in the Pampas region, Argentina. , 2017, The Science of the total environment.

[51]  Nadeem Faisal,et al.  Development of Pakistan’s New Area Weighted Rainfall Using Thiessen Polygon Method , 2012 .

[52]  Joana Hora Martins,et al.  A review of performance criteria to validate simulation models , 2015, Expert Syst. J. Knowl. Eng..

[53]  P. N. Suganthan,et al.  A Comparative Study of Empirical Mode Decomposition-Based Short-Term Wind Speed Forecasting Methods , 2015, IEEE Transactions on Sustainable Energy.

[54]  Mohsen Nasseri,et al.  Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network , 2008, Expert Syst. Appl..

[55]  S. Salma,et al.  Rainfall Trends in Different Climate Zones of Pakistan , 2012 .

[56]  Ashish Sharma,et al.  Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 3 — A nonparametric probabilistic forecast model , 2000 .

[57]  Pedro Antonio Gutiérrez,et al.  Simultaneous modelling of rainfall occurrence and amount using a hierarchical nominal-ordinal support vector classifier , 2014, Eng. Appl. Artif. Intell..

[58]  Oreste Reale,et al.  AIRS impact on analysis and forecast of an extreme rainfall event (Indus River Valley, Pakistan, 2010) with a global data assimilation and forecast system , 2012 .