A hadoop based weather prediction model for classification of weather data

Pandey, Anjali; Agrawal, Charul; Agrawal, Meena

doi:10.1109/icecct.2017.8117862

Cited by 20 publications

(5 citation statements)

References 5 publications

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“…Additionally, fuzzy logic and adaptive neuro fuzzy inference system (ANFIS) techniques were applied for precise forecasting of weather information based on mean square error. 8 It is very familiar that NWP models need high-tech computer to solve difficult scientific equations to attain a prediction dependent on climate conditions. Hewage et al proposed the new lightweight data-driven weather prediction model by analyzing temporal modeling techniques of long short-term memory (LSTM) and temporal convolutional networks (TCN).…”

Section: Related Workmentioning

confidence: 99%

“…The word count algorithm was utilized to discover the total condition of that day. Additionally, fuzzy logic and adaptive neuro fuzzy inference system (ANFIS) techniques were applied for precise forecasting of weather information based on mean square error 8 . It is very familiar that NWP models need high‐tech computer to solve difficult scientific equations to attain a prediction dependent on climate conditions.…”

Section: Related Workmentioning

confidence: 99%

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Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

Bhagavathi

Thavasimuthu

Murugesan

et al. 2021

Int J Communication

140

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In this research, a weather forecasting model based on machine learning is proposed for improving the accuracy and efficiency of forecasting. The aim of this research is to propose a weather prediction model for short-range prediction based on numerical data. Daily weather prediction includes the work of thousands of worldwide meteorologists and observers. Modernized computers make predictions more precise than ever, and earth-orbiting weather satellites capture pictures of clouds from space. However, in many cases, the forecast under many conditions is not accurate. Numerical weather prediction (NWP) is one of the popular methods for forecasting weather conditions. NWP is a major weather modeling tool for meteorologists which contributes to more accurate accuracy. In this research, the weather forecasting model uses the C5.0 algorithm with K-means clustering. The C5.0 is one of the better decision tree classifiers, and the decision tree is a great alternative for forecasting and prediction. The algorithm for clustering the K-means is used to combine identical data together. For this process, the clustering of K-means is initially applied to divide the dataset into the closest cluster of K. For training and testing, the meteorological data collection obtained from the database Modern-Era Historical Analysis for Research and Applications (MERRA) is used. The model's performance is assessed through MAE mean absolute error (MAE) and root mean square error (RMSE). And the proposed model is assessed with accuracy, sensitivity, and specificity for validation. The results obtained are compared with other current machine learning approaches, and the proposed model achieved predictive accuracy of 90.18%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

Bhagavathi

Thavasimuthu

Murugesan

et al. 2021

Int J Communication

140

View full text Add to dashboard Cite

show abstract

“…As shown in Fig. 1, the traditional centralized data quality control method is faced with many pressing challenges due to the fast acquisition frequency, complex echo characteristics and diverse interference echoes of weather radar data [55]. First, the effective radar interference echo identification and interference region filling are the basis of weather radar data quality control.…”

Section: Conceptualization Of Models Based On Edge-cloud Cooperationmentioning

confidence: 99%

Cloud–edge cooperation for meteorological radar big data: a review of data quality control

Zhang

et al. 2021

Complex Intell. Syst.

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With the rapid development of information technology construction, increasing specialized data in the field of informatization have become a hot spot for research. Among them, meteorological data, as one of the foundations and core contents of meteorological informatization, is the key production factor of meteorology in the era of digital economy as well as the basis of meteorological services for people and decision-making services. However, the existing centralized cloud computing service model is unable to satisfy the performance demand of low latency, high reliability and high bandwidth for weather data quality control. In addition, strong convective weather is characterized by rapid development, small convective scale and short life cycle, making the complexity of real-time weather data quality control increased to provide timely strong convective weather monitoring services. In order to solve the above problems, this paper proposed the cloud–edge cooperation approach, whose core idea is to effectively combine the advantages of edge computing and cloud computing by taking full advantage of the computing resources distributed at the edge to provide service environment for users to satisfy the real-time demand. The powerful computing and storage resources of the cloud data center are utilized to provide users with massive computing services to fulfill the intensive computing demands.

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“…Forecast precision can be affected by a variety of circumstances. Season, geographical location, input data accuracy, WX classifications, lead time, and validity time are some of these effective elements [20,21].…”

Section: Introductionmentioning

confidence: 99%

Weather Forecasting Using Radial Basis Function Neural Network in Warangal, India

Veeramsetty¹,

Kiran²,

Sushma³

et al. 2023

Urban Science

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Weather forecasting is an essential task in any region of the world for proper planning of various sectors that are affected by climate change. In Warangal, most sectors, such as agriculture and electricity, are mainly influenced by climate conditions. In this study, weather (WX) in the Warangal region was forecast in terms of temperature and humidity. A radial basis function neural network was used in this study to forecast humidity and temperature. Humidity and temperature data were collected for the period of January 2021 to December 2021. Based on the simulation results, it is observed that the radial basis function neural network model performs better than other machine learning models when forecasting temperature and humidity.

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A hadoop based weather prediction model for classification of weather data

Cited by 20 publications

References 5 publications

Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

Cloud–edge cooperation for meteorological radar big data: a review of data quality control

Weather Forecasting Using Radial Basis Function Neural Network in Warangal, India

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