Aranildo Lima

Aranildo Lima
Aquatic Informatics

PhD Atmospheric Science
ML applications on weather forecast, climate indexes, water quality, QC of water parameters (anomaly detection), etc.

About

28
Publications
2,750
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585
Citations

Publications

Publications (28)
Article
A hybrid algorithm combining support vector regression with evolutionary strategy (SVR-ES) is proposed for predictive models in the environmental sciences. SVR-ES uses uncorrelated mutation with p step sizes to find the optimal SVR hyper-parameters. Three environmental forecast datasets used in the WCCI-2006 contest – surface air temperature, preci...
Article
The extreme learning machine (ELM), a single-hidden layer feedforward neural network algorithm, was tested on nine environmental regression problems. The prediction accuracy and computational speed of the ensemble ELM were evaluated against multiple linear regression (MLR) and three nonlinear machine learning (ML) techniques - artificial neural net...
Article
While nonlinear machine methods have been widely used in environmental forecasting, in situations where new data arrive continuously, the need to make frequent model updates can become cumbersome and computationally costly. To alleviate this problem, an online sequential learning algorithm for single hidden layer feedforward neural networks – the o...
Article
Full-text available
Air quality data (observational and numerical) were used to produce hourly spot concentration forecasts of ozone (O3), particulate matter 2.5 μm (PM2.5), and nitrogen dioxide (NO2), up to 48 h for six stations across Canada—Vancouver, Edmonton, Winnipeg, Toronto, Montreal, and Halifax. Using numerical data from an air quality model (GEM-MACH15) as...
Conference Paper
Full-text available
The El Niño Southern Oscillation (ENSO) is the dominant mode of variability in the climate system on seasonal to decadal timescales. With foreknowledge of the state of ENSO, stakeholders can anticipate and mitigate impacts in climate-sensitive sectors such as agriculture and energy. Traditionally, ENSO forecasts have been produced using either comp...
Article
Full-text available
The worldwide appeal has increased for the development of new technologies that allow the use of green energy. In this category, photovoltaic energy (PV) stands out, especially with regard to the presentation of forecasting methods of solar irradiance or solar power from photovoltaic generators. The development of battery energy storage systems (BE...
Article
Full-text available
Solar irradiance forecasting has been an essential topic in renewable energy generation. Forecasting is an important task because it can improve the planning and operation of photovoltaic systems, resulting in economic advantages. Traditionally, single models are employed in this task. However, issues regarding the selection of an inappropriate mod...
Article
Full-text available
Stream temperature (Ts) is an important water quality parameter that affects ecosystem health and human water use for beneficial purposes. Accurate Ts predictions at different spatial and temporal scales can inform water management decisions that account for the effects of changing climate and extreme events. In particular, widespread predictions o...
Article
The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of...
Preprint
Full-text available
Starting from the Kaya identity, we used a Neural ODE model to predict the evolution of several indicators related to carbon emissions, on a country-level: population, GDP per capita, energy intensity of GDP, carbon intensity of energy. We compared the model with a baseline statistical model - VAR - and obtained good performances. We conclude that...
Article
In situations where new data arrive continually, online learning algorithms are computationally much less costly than batch learning ones in maintaining the model up-to-date. The extreme learning machine (ELM), a single hidden layer artificial neural network with random weights in the hidden layer, is solved by linear least squares, and has an onli...
Article
This paper proposes a method to perform time series prediction based on perturbation theory. The approach is based on continuously adjusting an initial forecasting model to asymptotically approximate a desired time series model. Firstly, a predictive model generates an initial forecasting for a time series. Secondly, a residual time series is calcu...
Article
In this work, we propose a hybrid algorithm combining support vector regression with evolutionary strategy (SVR-ES) in order to build successful predictive models for downscaling problems. SVR-ES uses uncorrelated mutation with p step sizes to find the optimal SVR hyper-parameters. Two downscaling forecast problems used in the WCCI-2006 contest - s...
Conference Paper
Artificial Neural Networks (ANN) have been widely used in order to solve the time series forecasting problem and one of its most promising approach is the combination with other intelligent techniques, such as genetic algorithms, evolutionary strategies, etc. The choice of a good fitness function still an open question for the practitioners who use...
Conference Paper
In this paper it is introduced a new perturbative approach for time series forecasting. The model uses the error of the series, that is the difference between real value of the series and the output of a predictive method, to improve the series forecasting. The methodology proposed is inspired in the Perturbation Theory, that consists in a set of a...
Chapter
Full-text available
In this chapter was presented a summary of how use the intelligent computational modelling for time series forecasting and the importance of the correct choice of the fitness function. Three methodology were employed for adjust the parameters of an ANN, a Modified Genetic Algorithm (MGA) (Section 4.2), a Particle Swarm Optimization (PSO) (Section 4...
Conference Paper
Full-text available
Forecasting systems have been widely used for decision making and one of its most promising approaches is based on Artificial Neural Networks (ANN). In this paper, a hybrid swarm system is presented for the time series forecasting problem, which consists of an intelligent hybrid model composed of an ANN combined with Particle Swarm Optimizer (PSO)....
Conference Paper
Artificial neural networks (ANN) have been widely used in order to solve the time series forecasting problem. One of its most promising approaches is the combination with other intelligence techniques, as genetic algorithms, evolutionary strategies, etc. The efficiency of these techniques, if used correctly, can be very high. Unfortunately, in term...
Conference Paper
The Perturbative Time-delay Added Evolutionary Forecasting (P-TAEF) method for time series prediction is inspired in the Perturbation Theory, a concept already commonly used in other areas of science (physics, mathematics, etc), and evolutionary computing. This methodology is shown and an experimental investigation is conducted with some relevant t...
Conference Paper
Full-text available
This paper presents an study about a new Hybrid method -GRASPES - for time series prediction, inspired in F. Takens theorem and based on a multi-start metaheuristic for combinatorial problems - Greedy Randomized Adaptive Search Procedure(GRASP) - and Evolutionary Strategies (ES) concepts. The GRAPES tuning and evolve the Artificial Neural Network p...
Conference Paper
This paper presents an study of a Hybrid method for time series prediction, called GRASPES, based on Greedy Randomized Adaptive Search Procedure (GRASP) Algorithm and Evolutionary Strategies (ES) concepts for tuning of the structure and parameters of an Artificial Neural Network (ANN). An experimental investigation with two time series is conducted...
Conference Paper
This paper presents an study of a new hybrid method based on the greedy randomized adaptive search procedure(GRASP) and evolutionary strategies(ES) concepts for tuning the structure and parameters of an artificial neural network (ANN). It consists of an ANN trained and adjusted by this new method, which searches for the minimum number of (and their...
Conference Paper
This work introduces a quantum-inspired intelligent hybrid (QIIH) method for stock market forecasting. It performs a quantum-inspired evolutionary search for the minimum necessary dimension (time lags) embedded in the problem for determining the characteristic phase space that generates the financial time series phenomenon. The proposed QIIH method...
Conference Paper
This paper proposes the Morphological-Rank-Linear Time-lag Added Evolutionary Forecasting (MRLTAEF) method for financial time series forecasting, which performs an evolutionary search for the minimum number of relevant time lags necessary to efficiently represent complex time series. It consists of an intelligent hybrid model composed of a Morpholo...
Article
This paper presents an new hybrid method for financial time series prediction called GRASPES. It is based on the Greedy Randomized Adaptive Search Procedure(GRASP), which is a multi-start metaheuristic for combinatorial problems, and Evolutionary Strategies (ES) concepts for tuning of the structure and parameters of an Artificial Neural Network (AN...

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