Weather Forecasting Using Machine Learning Algorithm

Singh, Nitin; Chaturvedi, Saurabh; Akhter, Shamim

doi:10.1109/icsc45622.2019.8938211

Cited by 70 publications

(16 citation statements)

References 3 publications

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“…4,5 Utilizing all the above techniques, forecasters propose their "best guess" concerning what conditions will be the weather throughout the following few days. Weather prediction now has a wide range of forecasting types that are classified as 6 follows:…”

Section: Computer Forecastingmentioning

confidence: 99%

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

Bhagavathi

Thavasimuthu

Murugesan

et al. 2021

Int J Communication

136

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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: Computer Forecastingmentioning

confidence: 99%

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

Bhagavathi

Thavasimuthu

Murugesan

et al. 2021

Int J Communication

136

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show abstract

“…Thus a comparison has been made between the forecast achievement of conditional restricted boltzmann machine (CRBM) with recurrence neural network (RNN) and convolutional network (CN) models. In [15] New machine learning techniques and data processing are presented by the authors (random forest classification) for climate conditions forecasting. They concentrate on the challenges of today's rapid weather change, which renders traditional weather prediction approaches less accurate and time-consuming.…”

Section: Related Workmentioning

confidence: 99%

“…Its architecture is depicted in Figure 4. GRNN replaces the sigmoid activation function typically used in ANN with a radial base function (RBF), which calculates the probability density function using an estimator [15]. The predicted value is simply a weighted average of target values from training patterns that are similar to the input pattern.…”

Section: Generalized Regression Neural Network (Grnn)mentioning

confidence: 99%

Predicting temperature of Erbil City applying deep learning and neural network

Jumur

Kareem

Yousif

2021

IJEECS

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<span>One of the most significant and daunting activities in today's world is temperature prediction. The meteorologists traditionally predict temperature via some statistical models aimed to forecast the fluctuations that might have happened to atmospheric parameters such as temperature, humidity, etc. The main objective of this paper is to build an intelligent temperature prediction model of Erbil city in KRG/ Iraq based on a historical dataset from 1992 to 2016 in each year there are twelve months’ average temperature readings from (January to December). Hence to resolve this prediction problem an up-to-date deep learning neural network has been used, the network model is based on <span id="docs-internal-guid-850bd062-7fff-c6c8-9146-ba6427eb24e0" style="font-size: 9pt; font-family: 'Times New Roman'; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">long short-term memory</span> (LSTM) as an artificial recurrent neural network (RNN) architecture which employed to estimate the future average temperature. The implementing model uses the dataset from real-time 30 weather stations deployed in the area of the city. The prediction performance of the proposed recurrent neural network model has been compared with some state of art algorithms like Adeline neural network, Autoregressive neural network (NAR), and <span id="docs-internal-guid-14d37b98-7fff-0f76-848f-ad9f89224f77" style="font-size: 9pt; font-family: 'Times New Roman'; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> generalized regression neural network</span> (GRNN). The results show that the proposed model based on deep learning gives minimum prediction error.</span>

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“…Singh et al [ 15 ] develops a low-cost, portable weather prediction system that can be used in remote areas, with data analysis and machine learning algorithms to predict weather conditions. The system architecture uses the Raspberry Pi as the main component with temperature, humidity, and barometric pressure sensors to obtain the sensed values and then train according to the random forest classification model, and predict whether it will rain.…”

Section: Introductionmentioning

confidence: 99%

“…The system architecture uses the Raspberry Pi as the main component with temperature, humidity, and barometric pressure sensors to obtain the sensed values and then train according to the random forest classification model, and predict whether it will rain. Note that although the system hardware in Singh et al [ 15 ] and that of the proposed weather monitoring and forecasting systems are similar, the system in Singh et al [ 15 ] only describes the probability of precipitation. In Varghese et al [ 16 ], with Raspberry Pi and weather sensors, data are collected, trained, and predicted using linear regression machine learning models for evaluation via mean absolute error and median absolute error.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Weather Monitoring and Prediction Using City Buses and Machine Learning

Huang

Chen²,

Wen

2020

Sensors

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Accurate weather data are important for planning our day-to-day activities. In order to monitor and predict weather information, a two-phase weather management system is proposed, which combines information processing, bus mobility, sensors, and deep learning technologies to provide real-time weather monitoring in buses and stations and achieve weather forecasts through predictive models. Based on the sensing measurements from buses, this work incorporates the strengths of local information processing and moving buses for increasing the measurement coverage and supplying new sensing data. In Phase I, given the weather sensing data, the long short-term memory (LSTM) model and the multilayer perceptron (MLP) model are trained and verified using the data of temperature, humidity, and air pressure of the test environment. In Phase II, the trained learning model is applied to predict the time series of weather information. In order to assess the system performance, we compare the predicted weather data with the actual sensing measurements from the Environment Protection Administration (EPA) and Central Weather Bureau (CWB) of Taichung observation station to evaluate the prediction accuracy. The results show that the proposed system has reliable performance at weather monitoring and a good forecast for one-day weather prediction via the trained models.

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Weather Forecasting Using Machine Learning Algorithm

Cited by 70 publications

References 3 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

Predicting temperature of Erbil City applying deep learning and neural network

Real-Time Weather Monitoring and Prediction Using City Buses and Machine Learning

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