Ensemble of evolving data clouds and fuzzy models for weather time series prediction

Soares, Eduardo; Costa, Paula Cristina Pereira da; Costa, Bruno Sielly Jales; Leite, Daniel

doi:10.1016/j.asoc.2017.12.032

Cited by 88 publications

(42 citation statements)

References 26 publications

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“…Carson reports 40 different results concerning χ s /ρ s c vs and points that 6 of them are connected with results which deviate from the others. e average of his other 34 measurements is χ s /ρ s c vs � 0.53 cm 2 /s [33][34][35][36][37][38]. It is expected that this work will be useful for people who are interested in studying and researching meteorological temperature and humidity prediction from Fourier-statistical analysis of hourly data.…”

Section: Discussionmentioning

confidence: 93%

“…e temperature distribution given by equation (34) should reduce at the surface of the ground to equation (27) and satisfy a limiting condition similar to that imposed by equation (28) and is represented by T(y, t) � T p (y), for large positive values of y. (35) e temperature changes at the surface of the ground should propagate upwards, to the atmosphere, and, although having pretty complicated details because of the convectionlike transport, should have, in the average, a simple spacetime behavior. For this reason, we assume that T a (y, t) may be written as: 8 Advances in Meteorology…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Meteorological Temperature and Humidity Prediction from Fourier-Statistical Analysis of Hourly Data

Castañeda-Miranda

Icaza-Herrera

Castaño

2019

Advances in Meteorology

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The temperature readings for all the 365 days and the 24 hours may be fitted through a 3 × 3 matrix (the so-called T-matrix). The mean square deviation between this fit and the actual meteorological measurements is smaller than three degrees Celsius. Four entries of this (nonsymmetric) matrix may be fixed by other means, leaving only five independent components. However, the same method applied to the humidity measurements produces a larger mean square deviation. A strong stochastical connection is found between the T-temperature matrix and the U-humidity matrix. The computer program, in C, may be used to adjust a (2M + 1) × (2m + 1) matrix simply by changing the arguments at the command line and has been tested with m and M ranging from zero to 11 (eleven) (more than 24 readings per day are necessary for larger values of m). The physical meaning of these constants is given only in the case m = M = 1. Our results have also been connected to fundamental cosmological properties: Earth’s orbit, the ecliptic angle, and the latitude of Querétaro (or whatever geographical location is chosen). A separate program calculates the angular position of the Sun as measured in the sky of Querétaro, to determine the length of the day or the mean value of the solar cosine. This work introduces several new variables which happen to be stochastically connected.

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Section: Discussionmentioning

confidence: 93%

Section: Resultsmentioning

confidence: 99%

Meteorological Temperature and Humidity Prediction from Fourier-Statistical Analysis of Hourly Data

Castañeda-Miranda

Icaza-Herrera

Castaño

2019

Advances in Meteorology

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“…Intelligent hybrid models were additionally used for the same purposes reported above by Lee and Liu [47] to weather forecasting, and an adaptive model for rain forecasting in Malaysia was created by El-Shafie et al [48]. It is also worth noting the recent model proposed by Ashrafi [49] for forecasting rainfall and river routing indices and the hybrid structure suggested by Soares et al [50] that works with the weather forecast series prediction in four Brazilian capitals (Sao Paulo, Manaus, Porto Alegre, and Natal). Therefore, there are no correlated works in the forecast of rainfall and temperatures in cities of the state of Minas Gerais, especially of its capital, using hybrid models.…”

Section: Intelligent Models Acting In the Prediction Of Temperatures mentioning

confidence: 99%

Fuzzy Rules to Help Predict Rains and Temperatures in a Brazilian Capital State Based on Data Collected from Satellites

2019

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Featured Application: A hybrid model formed by neural network techniques and fuzzy systems that predicts rainfall and temperatures in the capital of Minas Gerais, Brazil, which contains several river sources essential for the Brazilian economy. The purpose of the paper is to construct a model capable of extracting knowledge from satellite data related to temperatures and rainfall forecast of the analyzed state.Abstract: The forecast for rainfall and temperatures in underdevelope countries can help in the definition of public and private investment strategies in preventive and corrective nature. Water is an essential element for the economy and living things. This study had a main objective to use an intelligent hybrid model capable of extracting fuzzy rules from a historical series of temperatures and rainfall indices of the state of Minas Gerais in Brazil, more specifically in the capital. Because this is state has several rivers fundamental to the Brazilian economy, this study intended to find knowledge in the data of the problem to help public managers and private investors to act dynamically in the prediction of future temperatures and how they can interfere in the decisions related to the population of the state. The results confirm that the intelligent hybrid model can act with efficiency in the generation of predictions about the temperatures and average rainfall indices, being an efficient tool to predict the water situation in the future of this critical state for Brazil. Appl. Sci. 2019, 9, 5476 2 of 30 watersheds critical nationally. Along with several great cities, it has suffered a strong impact from drought [4].In contrast to the lack of water, it has problems with excess: floods and landslides [2]. If a river or stream receives a considerable amount of water from rain and cannot support it, it ends up overflowing and causing floods, which bring with it destruction, damage, and even death.The facts cited above are just some of the impacts that rain has on society. Within this context, this work was developed for the use of neural networks in the forecast of volumetric rainfall, based on a fuzzy rule system, developing a precise forecast, and contributing to the anticipation of possible tragedies s in places that occur phenomena of greater magnitude, as well as helping in a better control of agriculture. Another purpose was to support air and sea transportation, avoiding accidents in their trajectories. Briefly, when anticipating a possible problem, it is also possible to anticipate solutions and reduce their impacts, such as evacuating areas at risk of landslides or flooding [5].Currently, many kinds of research are being developed to apply models of artificial neural networks in rain forecasts, river basins, and water studies. For example, it was applied to the problem of forecasting the flow of the Nile River in Egypt [6], using a temporal series as a reference to compare tests among several prediction methods of neural networks. Besides, there was a study using stochastic autoregressive mean m...

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“…In this work input data is taken with the help of remote sensing device which is said to be remote sensed data. In order to select the relevant features for more accurate prediction, typicality-and-eccentricity-based evolving intelligent method was introduced in [3] using Spearman rank correlation. The method has a higher false positive rate in the weather time series prediction.…”

Section: Introductionmentioning

confidence: 99%

Point Biserial Correlated Feature Selection of Weather Data

S.B,

Balan

2019

IJEAT

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Big data is said to be huge amount of data. These data’s may be either structured or unstructured data. It is used for performing prediction in many fields and one among them is weather forecasting. Many feature selection techniques has been introduced but all these techniques failed to get accurate result. In order to improve weather prediction with less complexity, a Point Biserial Correlated Feature Selection (PBCFS) technique is introduced. The big weather dataset comprises the ‘n’ numbers of features. Initially, the PBCFS technique uses a point biserial correlation coefficient to determine relevant feature or irrelevant features among the several features. These relevant features which is selected with the help of this feature selection method can be used for clustering, classification or any other method to perform prediction. The polytomous (i.e. different classes) regression function analyzes the input data with the selected features to provide the significant results as output. Experimental evaluation of proposed PBCFS technique and existing methods are carried out using a big weather dataset. The result shows that we get the output with high feature selection accuracy.

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Ensemble of evolving data clouds and fuzzy models for weather time series prediction

Cited by 88 publications

References 26 publications

Meteorological Temperature and Humidity Prediction from Fourier-Statistical Analysis of Hourly Data

Meteorological Temperature and Humidity Prediction from Fourier-Statistical Analysis of Hourly Data

Fuzzy Rules to Help Predict Rains and Temperatures in a Brazilian Capital State Based on Data Collected from Satellites

Point Biserial Correlated Feature Selection of Weather Data

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