Downscaling of precipitation on a lake basin: evaluation of rule and decision tree induction algorithms

We investigate the performance of existing state-of-the-art rule induction and tree algorithms, namely Single Conjunctive Rule Learner, Decision Table, M5 Model Tree, Decision Stump and REPTree. Downscaling models are developed using these algorithms to obtain projections of mean monthly precipitation to lake-basin scale in an arid region in India. The effectiveness of these algorithms is evaluated through application to downscale the predictand for the Lake Pichola region in Rajasthan state in India, which is considered to be a climatically sensitive region. The predictor variables are extracted from (1) the National Centre for Environmental Prediction (NCEP) reanalysis dataset for the period 1948–2000 and (2) the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1 and COMMIT for the period 2001–2100. M5 Model Tree algorithm was found to yield better performance among all other learning techniques explored in the present study. The precipitation is projected to increase in future for A2 and A1B scenarios, whereas it is least for B1 and COMMIT scenarios using predictors.

[1]  K. Pearson.  Mathematical Contributions to the Theory of Evolution. III. Regression, Heredity, and Panmixia , 1896 .

[2]  Max F. Meyer,et al.  The Proof and Measurement of Association between Two Things. , 1904 .

[3]  Wm. R. Wright.  General Intelligence, Objectively Determined and Measured. , 1905 .

[4]  M. Kendall,et al.  Regression, structure and functional relationship. Part I. , 1951, Biometrika.

[5]  William D. Philpot,et al.  Small-Scale Climate Maps: A Sensitivity Analysis of Some Common Assumptions Associated with Grid-Point Interpolation and Contouring , 1985 .

[6]  D. Wilks.  Conditioning stochastic daily precipitation models on total monthly precipitation , 1989 .

[7]  M. Maccracken,et al.  The Use of General Circulation Models to Predict Regional Climatic Change , 1991 .

[8]  D. Wilks.  Adapting stochastic weather generation algorithms for climate change studies , 1992 .

[9]  J. R. Quinlan.  Learning With Continuous Classes , 1992 .

[10]  Robert G. Crane,et al.  Neural Nets: Applications in Geography , 1994 .

[11]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

[12]  Ron Kohavi,et al.  The Power of Decision Tables , 1995, ECML.

[13]  J. Ross Quinlan,et al.  Improved Use of Continuous Attributes in C4.5 , 1996, J. Artif. Intell. Res..

[14]  B. Hewitson,et al.  Climate downscaling: techniques and application , 1996 .

[15]  P. Jones,et al.  Precipitation and air flow indices over the British Isles , 1996 .

[16]  B. Hewitson,et al.  CROSS-SCALE RELATIONSHIPS REGARDING LOCAL TEMPERATURE INVERSIONS AT CAPE TOWN AND GLOBAL CLIMATE CHANGE IMPLICATIONS , 1996 .

[17]  H. Storch,et al.  Statistical downscaling of monthly mean North Atlantic air-pressure to sea level anomalies in the Baltic Sea , 1996 .

[18]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[19]  R. Hosier.  Climate variability, climate change and social vulnerability in the semi-arid tropics , 1997 .

[20]  T. Wigley,et al.  Downscaling general circulation model output: a review of methods and limitations , 1997 .

[21]  G. Kite.  Simulating Columbia River flows with data from regional‐scale climate models , 1997 .

[22]  P. Jones,et al.  Predicting rainfall statistics in England and Wales using atmospheric circulation variables , 1998 .

[23]  M. W Gardner,et al.  Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences , 1998 .

[24]  C. Goodess,et al.  DEVELOPMENT OF DAILY RAINFALL SCENARIOS FOR SOUTHEAST SPAIN USING A CIRCULATION-TYPE APPROACH TO DOWNSCALING , 1998 .

[25]  Chong-yu Xu.  From GCMs to river flow: a review of downscaling methods and hydrologic modelling approaches , 1999 .

[26]  Chris Kilsby,et al.  A stochastic rainfall model for the assessment of regional water resource systems under changed climatic condition , 2000 .

[27]  James M. Murphy,et al.  Predictions of climate change over Europe using statistical and dynamical downscaling techniques , 2000 .

[28]  Alex J. Cannon,et al.  Forecasting Summertime Surface-Level Ozone Concentrations in the Lower Fraser Valley of British Columbia: An Ensemble Neural Network Approach , 2000, Journal of the Air & Waste Management Association.

[29]  Deliang Chen,et al.  Comparison of climate change scenarios for Sweden based on statistical and dynamical downscaling of monthly precipitation , 2001 .

[30]  Raquel V. Francisco,et al.  Regional Climate Information—Evaluation and Projections , 2001 .

[31]  S. Pryor,et al.  Downscaling temperature and precipitation: a comparison of regression‐based methods and artificial neural networks , 2001 .

[32]  Christel Prudhomme,et al.  Uncertainty and climate change impact on the flood regime of small UK catchments , 2003 .

[33]  Tammo S. Steenhuis,et al.  Application of two hydrologic models with different runoff mechanisms to a hillslope dominated watershed in the northeastern US: a comparison of HSPF and SMR , 2003 .

[34]  Simon J. Mason,et al.  Simulating Climate over Western North America Using Stochastic Weather Generators , 2004 .

[35]  Dimitri P. Solomatine,et al.  M5 Model Trees and Neural Networks: Application to Flood Forecasting in the Upper Reach of the Huai River in China , 2004 .

[36]  Martin Dubrovský,et al.  High-Frequency and Low-Frequency Variability in Stochastic Daily Weather Generator and Its Effect on Agricultural and Hydrologic Modelling , 2004 .

[37]  L. Ruby Leung,et al.  Regional climate modeling: Progress, challenges, and prospects , 2004 .

[38]  A. Bardossy,et al.  Fuzzy rule-based downscaling of precipitation , 2005 .

[39]  Deliang Chen,et al.  Statistical downscaling of climate scenarios over Scandinavia , 2005 .

[40]  Dimitri P. Solomatine,et al.  Neural networks and M5 model trees in modelling water level-discharge relationship , 2005, Neurocomputing.

[41]  Balaji Rajagopalan,et al.  Statistical downscaling using K‐nearest neighbors , 2005 .

[42]  Chong-Yu Xu,et al.  Statistical precipitation downscaling in central Sweden with the analogue method , 2005 .

[43]  Paulin Coulibaly,et al.  Temporal neural networks for downscaling climate variability and extremes , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[44]  P. P. Mujumdar,et al.  Future rainfall scenario over Orissa with GCM projections by statistical downscaling , 2006 .

[45]  Dominik Heckmann,et al.  Weka: Practical machine learning tools and techniques with Java implementations , 2007 .

[46]  V. V. Srinivas,et al.  Downscaling of precipitation for climate change scenarios: A support vector machine approach , 2006 .

[47]  Mohd Fauzi Othman,et al.  Comparison of different classification techniques using WEKA for breast cancer , 2007 .

[48]  Hayley J. Fowler,et al.  Beyond the downscaling comparison study , 2007 .

[49]  Stefano Schiavon,et al.  Climate Change 2007: The Physical Science Basis. , 2007 .

[50]  Katharine Hayhoe,et al.  A general method for validating statistical downscaling methods under future climate change , 2007 .

[51]  Rasmus E. Benestad,et al.  An evaluation of statistical models for downscaling precipitation and their ability to capture long‐term trends , 2007 .

[52]  S. Solomon.  The Physical Science Basis : Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change , 2007 .

[53]  Roland K. Price,et al.  Machine Learning Approach to Modeling Sediment Transport , 2007 .

[54]  Chong-Yu Xu,et al.  Seasonality properties of four statistical-downscaling methods in central Sweden , 2007 .

[55]  Daud Mnr.  Human Readable Rule Induction in Medical Data Mining: A Survey of Existing Algorithms , 2007 .

[56]  Multimodel output statistical downscaling prediction of precipitation in the Philippines and Thailand , 2007 .

[57]  Hayley J. Fowler,et al.  Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling , 2007 .

[58]  C. Tam,et al.  Seasonal forecast for local precipitation over northern Taiwan using statistical downscaling , 2008 .

[59]  Subimal Ghosh,et al.  Statistical downscaling of GCM simulations to streamflow using relevance vector machine , 2008 .

[60]  Ravi S. Nanjundiah,et al.  Role of predictors in downscaling surface temperature to river basin in India for IPCC SRES scenarios using support vector machine , 2009 .

[61]  Ewan Klein,et al.  Natural Language Processing with Python , 2009 .

[62]  A. Etemad-Shahidi,et al.  COMPARISON BETWEEN M5 MODEL TREE AND NEURAL NETWORKS FOR PREDICTION OF SIGNIFICANT WAVE HEIGHT IN LAKE SUPERIOR , 2009 .

[63]  Vijay P. Singh,et al.  Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China , 2009 .

[64]  C. Ojha,et al.  Downscaling of Precipitation for Lake Catchment in Arid Region in India using Linear Multiple Regression and Neural Networks , 2010 .

[65]  C. Spearman.  The proof and measurement of association between two things. , 2015, International journal of epidemiology.

[66]  R. Naylor,et al.  Downscaling Indonesian precipitation using large‐scale meteorological fields , 2010 .

[67]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[68]  Manish Kumar Goyal,et al.  PLS regression-based pan evaporation and minimum–maximum temperature projections for an arid lake basin in India , 2011 .

[69]  D. Burn,et al.  Nonparametric Statistical Downscaling of Temperature, Precipitation, and Evaporation in a Semiarid Region in India , 2012 .

[70]  Manish Kumar Goyal,et al.  Downscaling of surface temperature for lake catchment in an arid region in India using linear multiple regression and neural networks , 2012 .

[71]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .