author = "Anochi, Juliana Aparecida and Campos Velho, Haroldo Fraga de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Mesoscale precipitation climate prediction for brazilian south 
                         region by artificial neural networks",
              journal = "American Journal of Environmental Engineering",
                 year = "2016",
               volume = "6",
               number = "4",
                pages = "94--102",
             keywords = "Climate prediction, Precipitation, Self-configured neural network, 
                         Data reduction.",
             abstract = "Numerical weather and climate use sophisticated mathematical 
                         models. These models are employed to simulate the atmospheric 
                         dynamics to perform a medium-range forecasting and climate 
                         prediction. Such an approach allows to estimate all meteorological 
                         variables for a future time period: wind fields, air temperature, 
                         pressure, moisture, and precipitation field. Precipitation is one 
                         of the most difficult fields for prediction. The latter statement 
                         is verified due to high variability in space and time. However, 
                         precipitation is a key issue in many activities of society. An 
                         alternative approach for climate prediction to the precipitation 
                         field is to employ the Artificial Neural Network (ANN). Such 
                         technique has a reduced computational cost in comparison with time 
                         integration of the partial differential equations. One challenge 
                         to employ an ANN is to determine the topology or configuration of 
                         a neural network. Here, a supervised ANN is designed to perform 
                         the precipitation prediction looking at two different periods: 
                         monthly and seasonal precipitation. The method is applied to the 
                         Southern region of Brazil. The definition of the neural network 
                         topology is addressed as an optimization problem. The best 
                         configuration is computed by minimizing a cost function. The 
                         optimization problem is solved by a new meta-heuristic: 
                         Multi-Particle Collision Algorithm (MPCA). In addition, a 
                         technique based on rough set theory is used to reduce the data 
                         space dimension. The predicted precipitation is evaluated by 
                         comparison with measured data. The prediction is also evaluated 
                         using full and reduced input data for a neural predictive model.",
                  doi = "10.5923/s.ajee.201601.14",
                  url = "http://dx.doi.org/10.5923/s.ajee.201601.14",
                 issn = "2166-4633 and 2166-465X",
                label = "lattes: 2720072834057575 1 AnochiCamp:2016:MePrCl",
             language = "en",
                  url = "http://article.sapub.org/10.5923.s.ajee.201601.14.html",
        urlaccessdate = "02 dez. 2020"