@inproceedings{scholars1620,
         journal = {2011 National Postgraduate Conference - Energy and Sustainability: Exploring the Innovative Minds, NPC 2011},
         address = {Perak},
           title = {Intelligent methods for weather forecasting: A review},
            note = {cited By 23; Conference of 3rd National Postgraduate Conference - Energy and Sustainability: Exploring the Innovative Minds, NPC 2011 ; Conference Date: 19 September 2011 Through 20 September 2011; Conference Code:88531},
            year = {2011},
             doi = {10.1109/NatPC.2011.6136289},
            isbn = {9781457718847},
          author = {Saima, H. and Jaafar, J. and Belhaouari, S. and Jillani, T. A.},
        abstract = {Weather forecasting is one of the most important and challenging field for scientists and engineers. The advent of technology has enabled us to obtain forecasts using complex mathematical models. For the last three decades, artificial intelligent based learning models like neural networks, genetic algorithms and neuro-fuzzy logic have shown much better results as compared to Box-Cox modeling approaches. Further accuracy is expectable by constructing a consortium of statistical and artificial intelligent methods. For weather forecasting, researcher's trend is also towards the hybrid models. The accuracy of forecasting models can be made using different measures of assessments. In this paper, some hybrid methods are discussed with their merits and demerits. {\^A}{\copyright} 2011 IEEE.},
        keywords = {Artificial intelligent; Forecasting models; Hybrid method; Hybrid model; Intelligent method; Learning models; Modeling approach; Neuro-fuzzy logic; Scientists and engineers; type-2 fuzzy, Artificial intelligence; Fuzzy logic; Mathematical models; Measurement errors; Sustainable development, Weather forecasting},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857089802&doi=10.1109\%2fNatPC.2011.6136289&partnerID=40&md5=782725f7670a84f8734f64726ef70666}
}