Aranildo R. Lima

发表

An intelligent perturbative approach for the time series forecasting problem

George D. C. Cavalcanti, Paulo S. G. de Mattos Neto, Aranildo R. Lima, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Variable complexity online sequential extreme learning machine, with applications to streamflow prediction

Aranildo R. Lima, William W. Hsieh, Alex J. Cannon, 2017 .

Downscaling temperature and precipitation using support vector regression with evolutionary strategy

Aranildo R. Lima, William W. Hsieh, Alex J. Cannon, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods

Aranildo R. Lima, Jian Jin, William W. Hsieh, 2017, Air Quality, Atmosphere & Health.

Nonlinear regression in environmental sciences by support vector machines combined with evolutionary strategy

Aranildo R. Lima, William W. Hsieh, Alex J. Cannon, 2013, Comput. Geosci..

The 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia, June 10-15, 2012

Aranildo R. Lima, Wei Wang, Brendan McCane, 2012, IJCNN.

Extreme Learning Machines for Environmental Problems

Aranildo R. Lima, 2013 .

A perturbative approach for enhancing the performance of time series forecasting

George D. C. Cavalcanti, Paulo S. G. de Mattos Neto, Aranildo R. Lima, 2017, Neural Networks.

Forecasting daily streamflow using online sequential extreme learning machines

Aranildo R. Lima, William W. Hsieh, Alex J. Cannon, 2016 .

Nonlinear regression in environmental sciences using extreme learning machines: A comparative evaluation

Aranildo R. Lima, William W. Hsieh, Alex J. Cannon, 2015, Environ. Model. Softw..

Tests With Different Fitness Functions For Tuning Of Artificial Neural Networks With Genetic Algorithms

Aranildo R. Lima, D. A. Silva, T. Ferreira, 2016 .

Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?

Aranildo R. Lima, J. Willard, J. Zwart, 2022, Hydrological Processes.

Stock Market Simulation for Volatility Analysis Inspired on Ideal Gas: an Intelligent Agent Approach

George D. C. Cavalcanti, Aranildo R. Lima, T. Ferreira, 2016 .

Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning

Aranildo R. Lima, J. Willard, C. Varadharajan, 2022, Water.

Forecasting El Niño with Convolutional and Recurrent Neural Networks

Aranildo R. Lima, A. Mahesh, M. Castillo, 2019 .

Solar Irradiance Forecasting Using Dynamic Ensemble Selection

Aranildo R. Lima, Manoel H. N. Marinho, Paulo S. G. de Mattos Neto, 2022, Applied Sciences.

Forecasting emissions through Kaya identity using Neural Ordinary Differential Equations

Aranildo R. Lima, Rossella Arcucci, Pierre Browne, 2022, ArXiv.

Forecasting Methods for Photovoltaic Energy in the Scenario of Battery Energy Storage Systems: A Comprehensive Review

Aranildo R. Lima, Paulo S. G. de Mattos Neto, J. F. D. de Oliveira, 2023, Energies.

A Comparison of Data-Driven Models for Predicting Stream Water Temperature

Aranildo R. Lima, B. Faybishenko, C. Varadharajan, 2020 .