A gene-wavelet model for long lead time drought forecasting

Summary Drought forecasting is an essential ingredient for drought risk and sustainable water resources management. Due to increasing water demand and looming climate change, precise drought forecasting models have recently been receiving much attention. Beginning with a brief discussion of different drought forecasting models, this study presents a new hybrid gene–wavelet model, namely wavelet–linear genetic programing (WLGP), for long lead-time drought forecasting. The idea of WLGP is to detect and optimize the number of significant spectral bands of predictors in order to forecast the original predictand (drought index) directly. Using the observed El Nino–Southern Oscillation indicator (NINO 3.4 index) and Palmer’s modified drought index (PMDI) as predictors and future PMDI as predictand, we proposed the WLGP model to forecast drought conditions in the State of Texas with 3, 6, and 12-month lead times. We compared the efficiency of the model with those of a classic linear genetic programing model developed in this study, a neuro-wavelet (WANN), and a fuzzy-wavelet (WFL) drought forecasting models formerly presented in the relevant literature. Our results demonstrated that the classic linear genetic programing model is unable to learn the non-linearity of drought phenomenon in the lead times longer than 3 months; however, the WLGP can be effectively used to forecast drought conditions having 3, 6, and 12-month lead times. Genetic-based sensitivity analysis among the input spectral bands showed that NINO 3.4 index has strong potential effect in drought forecasting of the study area with 6–12-month lead times.

[1]  Daniel Rivero,et al.  Determination of the unit hydrograph of a typical urban basin using genetic programming and artificial neural networks , 2007 .

[2]  Stéphane Mallat,et al.  A Wavelet Tour of Signal Processing, 2nd Edition , 1999 .

[3]  Vahid Nourani,et al.  Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling , 2013 .

[4]  Milan Onderka,et al.  Seepage velocities derived from thermal records using wavelet analysis , 2013 .

[5]  R. Heim.  A Review of Twentieth-Century Drought Indices Used in the United States , 2002 .

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

[7]  Anteneh Meshesha Belayneh,et al.  Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression , 2012, Appl. Comput. Intell. Soft Comput..

[8]  Daniel Rivero,et al.  Prediction and modeling of the rainfall-runoff transformation of a typical urban basin using ann and gp , 2003, Appl. Artif. Intell..

[9]  Z. Sen,et al.  Critical drought analysis by second-order Markov chain. , 1990 .

[10]  Mohammad H. Aminfar,et al.  A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation , 2009, Eng. Appl. Artif. Intell..

[11]  J. Dracup,et al.  The influences of Type 1 El Nino and La Nina events on streamflows in the Pacific Southwest of the United States , 1994 .

[12]  Mahmut Firat,et al.  Adaptive Neuro-Fuzzy Inference System for drought forecasting , 2009 .

[13]  C. L. Wu,et al.  Methods to improve neural network performance in daily flows prediction , 2009 .

[14]  Vahid Nourani,et al.  A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling , 2009 .

[15]  Geza Pesti,et al.  A fuzzy rule-based approach to drought assessment , 1996 .

[16]  Thomas C. Piechota,et al.  Drought and Regional Hydrologic Variation in the United States: Associations with the El Niño-Southern Oscillation , 1996 .

[17]  D. Labat,et al.  Rainfall-runoff relations for karstic springs. Part II: Continuous wavelet and discrete orthogonal multiresolution analyses. , 2000 .

[18]  Riccardo Poli,et al.  A Field Guide to Genetic Programming , 2008 .

[19]  S. Morid,et al.  Drought forecasting using artificial neural networks and time series of drought indices , 2007 .

[20]  Jan Adamowski,et al.  Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models , 2014 .

[21]  Peter A. Whigham,et al.  Modelling rainfall-runoff using genetic programming , 2001 .

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

[23]  Eduardo A. Varas,et al.  Artificial neural networks for streamflow prediction , 2002 .

[24]  V. Singh,et al.  Drought modeling-A review , 2011 .

[25]  Ali Danandeh Mehr,et al.  Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique , 2013 .

[26]  Vahid Nourani,et al.  Hybrid Wavelet-Genetic Programming Approach to Optimize ANN Modeling of Rainfall-Runoff Process , 2012 .

[27]  David Labat,et al.  Recent advances in wavelet analyses: Part 1. A review of concepts , 2005 .

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

[29]  I. Bogardi,et al.  Application of fuzzy rule-based modeling technique to regional drought , 1999 .

[30]  Vahid Nourani,et al.  Geomorphology-based genetic programming approach for rainfall–runoff modeling , 2013 .

[31]  A. Bárdossy,et al.  A hydroclimatological model of areal drought , 1994 .

[32]  Juan B. Valdés,et al.  NONLINEAR MODEL FOR DROUGHT FORECASTING BASED ON A CONJUNCTION OF WAVELET TRANSFORMS AND NEURAL NETWORKS , 2003 .

[33]  Ozgur Kisi,et al.  Stream flow forecasting using neuro‐wavelet technique , 2008 .

[34]  Lamont-Doherty Earth Observatory.  Spatiotemporal Variability of ENSO and SST Teleconnections to Summer Drought over the United States during the Twentieth Century , 2022 .

[35]  O. Kisi,et al.  Wavelet and neuro-fuzzy conjunction model for precipitation forecasting , 2007 .

[36]  Vijay P. Singh,et al.  Low frequency drought variability associated with climate indices , 2009 .

[37]  Peter Nordin,et al.  Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications , 1998 .

[38]  Vijay P. Singh,et al.  Long Lead Time Drought Forecasting Using a Wavelet and Fuzzy Logic Combination Model: A Case Study in Texas , 2012 .

[39]  Peter J. Webster,et al.  PREDICTION OF MONSOON RAINFALL AND RIVER DISCHARGE ON 15-30-DAY TIME SCALES , 2004 .

[40]  A. Mishra,et al.  Drought forecasting using feed-forward recursive neural network , 2006 .

[41]  Ozgur Kisi,et al.  Evapotranspiration Modeling Using Linear Genetic Programming Technique , 2010 .

[42]  A. Cancelliere,et al.  Drought forecasting using the Standardized Precipitation Index , 2007 .

[43]  Mohammad Ali Ghorbani,et al.  Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks , 2010, Comput. Geosci..

[44]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[45]  Ercan Kahya,et al.  U.S. streamflow patterns in relation to the El Niño/Southern Oscillation , 1993 .

[46]  Ali Danandeh Mehr,et al.  Successive-station monthly streamflow prediction using neuro-wavelet technique , 2014, Earth Science Informatics.

[47]  Ercan Kahya,et al.  The relationships between U.S. streamflow and La Niña Events , 1994 .

[48]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[49]  A. Mishra,et al.  Drought forecasting using stochastic models , 2005 .

[50]  V. Lohani,et al.  AN EARLY WARNING SYSTEM FOR DROUGHT MANAGEMENT USING THE PALMER DROUGHT INDEX 1 , 1997 .

[51]  V. Singh,et al.  A review of drought concepts , 2010 .

[52]  R. Modarres.  Streamflow drought time series forecasting , 2007 .

[53]  A. Ramachandra Rao,et al.  Analysis and modeling of Palmer's drought index series , 1984 .

[54]  Gavin J. Bowden,et al.  Toward long-lead operational forecasts of drought: An experimental study in the Murray-Darling River Basin , 2008 .

[55]  V. Singh,et al.  Drought Forecasting Using a Hybrid Stochastic and Neural Network Model , 2007 .

[56]  Mehmet Özger,et al.  Significant wave height forecasting using wavelet fuzzy logic approach , 2010 .

[57]  C. Fernández,et al.  Streamflow drought time series forecasting: a case study in a small watershed in North West Spain , 2009 .