Short-term rainfall prediction using ANN and MT techniques

References

• Applequist , S , Gahrs , GE , Pfeffer , RL and Xu-Feng , Niu . 2002 . Comparisons of methodologies for probabilistic quantitative precipitation forecasting . Weather Forecast. , 17 ( 4 ) : 783 – 799 .
   Web of Science ®Google Scholar
• ASCE . 2000a . Artificial neural network in hydrology, II: preliminary concepts . J. Hydrol. Eng. , 5 ( 2 ) : 124 – 137 .
   Web of Science ®Google Scholar
• ASCE . 2000b . Artificial neural network in hydrology: preliminary concepts . J. Hydrol. Eng. , 5 ( 2 ) : 115 – 123 .
   Web of Science ®Google Scholar
• Bhattacharya , B and Solomatine , DP . 2005 . Neural networks and M5 model trees in modelling water level-discharge relationship . Neurocomputing , 63 : 381 – 396 .
   Web of Science ®Google Scholar
• Bustamnante , JF . J.L. Gomes, Chou, S.C., and Rozant, J.R. (1999). “Evaluation of April 1999 rainfall forecasts over South America using the ETA model.” Climanalise, Divulgacao, Cientifica. INPE, Cachoeira, SP, Brazil
   Google Scholar
• Chou , SC . and Justi da Silva, M. (1999). “Objective evaluation of ETA model precipitation forecasts over South America.” Climanalise. INPE, Cachoeira, SP, Brazil
   Google Scholar
• de Vos , NJ and Rientjes , THM . 2005 . Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation . Hydrol. Earth Syst. Sci. , 9 : 111 – 126 .
   Web of Science ®Google Scholar
• French , MN , Krajewski , WF and Cuykendall , RR . 1992 . Rainfall forecasting in space and time using a neural network . J. Hydrol. , 137 ( 1–4 ) : 1 – 37 .
   Web of Science ®Google Scholar
• Gwangseob , K and Barros , AP . 2001 . Quantitative flood forecasting using multisensory data and neural networks . J. Hydrol. , 246 ( 1–4 ) : 45 – 62 .
   Web of Science ®Google Scholar
• Haykin , S . (1999). Neural network: a comprehensive foundation. Prentice-Hall, Upper Saddle River, NJ
   Google Scholar
• Jothiprakash , V and Kote , AS . 2010 . Effect of pruning and smoothing while using M5 model tree technique for reservoir inflow prediction . J. Hydrol. Eng. , 16 ( 7 ) : 567 – 574 .
   Web of Science ®Google Scholar
• Jothiprakash , V and Kote , AS . 2011 . Improving the performance of data-driven techniques through data pre-processing for modelling daily reservoir inflow . Hydrol. Sci. J. , 56 ( 1 ) : 168 – 186 .
   Web of Science ®Google Scholar
• Koizumi , K . 1999 . An objective method to modify numerical model forecasts with newly given weather data using an artificial neural network . Weather Forecast. , 14 ( 1 ) : 109 – 118 .
   Web of Science ®Google Scholar
• Kompare , B , Steinman , F , Cerar , U and Dzeroski , S . 1997 . Prediction of rainfall runoff from catchment by intelligent data analysis with machine learning tools within the artificial intelligence tools . Acta Hydrotechnica. , 16/17 : 79 – 94 .
   Google Scholar
• Kuligowski , R and Barros , AP . 1998 . Localized precipitation forecast from a numerical weather prediction model using artificial neural network . Weather Forecast. , 13 ( 4 ) : 1194 – 1204 .
   Web of Science ®Google Scholar
• Luk , KG , Ball , JE and Sharma , A . 2000 . Study of optimal lag and statistical inputs to artificial neural network for rainfall forecasting . J. Hydrol. , 227 : 56 – 65 .
   Web of Science ®Google Scholar
• Luk , KG , Ball , JE and Sharma , A . 2001 . An application of artificial neural network for rainfall forecasting . Math. Comput. Modeling. , 33 ( 6–7 ) : 683 – 693 .
   Google Scholar
• Maier , HER and Dandy , OK . 2000 . Neural networks for the predictions and forecasting of water resources variables: Review of modeling of issues and applications . Environ. Model. Software. , 15 ( 1 ) : 101 – 124 .
   Web of Science ®Google Scholar
• Nash , JE and Sutcliffe , JV . 1970 . River flow forecasting through conceptual models, Part 1—a discussion of principles . J. Hydrol. , 10 ( 3 ) : 282 – 290 .
   Google Scholar
• Nasseri , M , Shari , K and Abiding , MR . 2008 . Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network . Expert Syst. Appl. , 35 ( 3 ) : 1415 – 1421 .
   Web of Science ®Google Scholar
• Navone , HAD and Ceccatto , HAD . 1994 . Predicting Indian monsoon rainfall: a neural network approach . Clim. Dyn. , 10 ( 6–7 ) : 305 – 312 .
   Web of Science ®Google Scholar
• Quinlan , JR . (1992). Learning with continuous classes. Proc. 5th Australian Joint Conf. on Artificial Intelligence, A. Adams and L. Sterling, eds., World Scientific, Singapore, 343–348
   Google Scholar
• Ramirez , MCV , Velho , HFC and Ferreira , NJ . 2005 . Artificial neural network technique for rainfall forecasting applied to the Sao Paulo region . J. Hydrol. , 301 ( 1–4 ) : 146 – 162 .
   Web of Science ®Google Scholar
• Solomatine , DP and Dulal , KN . 2003 . Model trees as an alternative to neural networks in rainfall-runoff modeling . Hydrol. Sci. J. , 48 ( 3 ) : 399 – 411 .
   Web of Science ®Google Scholar
• Solomatine , DP and Xue , Y . 2004 . M5 model trees and neural networks: application to flood forecasting in the upper reach of the Huai River in China . J. Hydrol. Eng. , 9 : 491 – 501 .
   Web of Science ®Google Scholar
• Tippett , M . and da Silva, A. (1999). “Model error estimations for the CPTEC ETA model.” Climanalise, Divulgacao, Cientifica. INPE, Cachoeira, SP, Brazil
   Google Scholar
• Witten , H . and Frank, E. (2000). Data mining: practical machine learning tools and techniques with Java implementation. Morgan Kaufmann Publishers, San Mateo, CA
   Google Scholar