Temperature Prediction using Machine Learning Approaches

Anjali, T; Chandini, K; Anoop, K; Lajish, V L

doi:10.1109/icicict46008.2019.8993316

Cited by 24 publications

(7 citation statements)

References 9 publications

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“…Tey found that the RF model outperformed the linear model and that anomalies in sea surface temperature were important predictors of central European summer heat waves. Sharaf and Roy [53] and Anjali et al [54] analyzed the performance of multilinear regression (MLR), support vector machine (SVM), ANN, and regression tree methods to predict daily temperature values using data collected from Mumbai Chhatrapati Shivaji Airport (2001-2016) and Central Kerala (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015). Tey compared the results based on mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and correlation coefcient metrics.…”

Section: Related Workmentioning

confidence: 99%

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Subseasonal Prediction of Summer Temperature in West Africa Using Artificial Intelligence: A Case Study of Senegal

Kenne,

Toure,

Logamou Seknewna

et al. 2024

International Journal of Intelligent Systems

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Despite the rapid growth of machine learning (ML) and its far-reaching applications in various fields such as healthcare, finance, and urban heat management, there are still some unresolved challenges in the field of climate change. Reliable subseasonal forecasts of summer temperatures would be a great benefit to society. Although numerical weather prediction (NWP) models are better at capturing relevant sources of predictability, such as temperatures, land, and sea surface conditions, the subseasonal potential is not fully exploited. One such challenge is accurate subseasonal temperature forecasting using cutting-edge ML technology. This study aims to assess and predict the changes in subseasonal temperature during the summer season (from March to June) in Senegal on 2-weeks time scales. Six ML techniques, including linear regression (LR), decision tree (DT), support vector machine (SVM), artificial neural network (ANN), long short-term memory (LSTM), and gated recurrent units (GRU), are used. The experiments utilize a multivariate approach by incorporating variables of the ERA-5 dataset from 1981 to 2022. The results compared all the performances of the methods to assess their overall effectiveness in forecasting air temperature (t2m) values over 2 weeks. Our analysis demonstrates that the GRU model outperforms the other ML models, achieving a Nash–Sutcliffe efficiency (NSE) score of 74.68% and a mean absolute percentage error (MAPE) of 2.51%. The GRU model effectively captures long-term dependencies and exhibits superior performance in temperature forecasting. Furthermore, a comparison between the observed and predicted values confirms the accuracy of the GRU model in aligning with actual temperature trends. Overall, this study contributes an impactful deep learning model to the field of subseasonal temperature forecasting in West Africa (Senegal), which offers local authorities the capability to anticipate climatic events and enact preventive measures accordingly.

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Section: Related Workmentioning

confidence: 99%

“…where w controls the smoothness of the model ξ i , ξ i * are slack variables ϕ is the function of projection of the input space to the feature space b is the parameter of bias y i is the target value to be estimated [38,44,54]. Te error backpropagation learning algorithm, commonly known as the least-mean-square algorithm (L.M.S.…”

Section: Support Vectormentioning

confidence: 99%

Subseasonal Prediction of Summer Temperature in West Africa Using Artificial Intelligence: A Case Study of Senegal

Kenne,

Toure,

Logamou Seknewna

et al. 2024

International Journal of Intelligent Systems

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“…Vector Machine (SVM). Statistical analysis of models suggested that MLR better works than ANN and SVM (Anjali et al, 2019). A mathematical model Artificial Neural Networking Multilayer Perceptron (ANN-MLP), and two statistical models, Exponential Smoothing Algorithm (ETS) and Auto-Regressive Integrated Moving Average (ARIMA), were designed to predict meteorological parameters temperature, humidity, and wind speed of Lahore (2017 -2018), Pakistan.…”

Section: Literature Reviewmentioning

confidence: 99%

Temperature data of Hyderabad from the temperature of three neighboring cities using the ANN and the multiple regression methods

Tahir¹,

Ashraf²,

Razzak³

et al. 2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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The notion of this research was to find the climatological parameter of a location (whose data are notavailable) with the help of climatological parameters of its neighboring locations. In this study, we supposed Hyderabad as such a location; its neighboring cities Karachi, Badin, and Nawabshah form a triangle, and Hyderabad lies within the perimeter of the triangle. The task was to find the temperature distribution of Hyderabad using the temperatures of its neighboring cities. Two different methods, Artificial Neural Networks and multiple regression analysis, have been used to find the temperature of Hyderabad by using the temperature distribution of neighboring cities, Karachi,Nawabshah, and Badin. ANN with one hidden layer of 10 neurons is used, which connect three known temperature of Karachi, Nawabshah, and Badin to the output temperature of Hyderabad. Amultiple regression analysis with three independent variables, i.e., the temperatures of Karachi,Nawabshah, and Badin, are used to find a linear equation of multiple variables for the temperature of Hyderabad.Both ANN and multiple regression analysis produce an excellent result, and it is suggested toimplement the technique where direct measurement of the climatological data system is not present.

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“…Recently, deep learning models have been applied to the prediction of time series problems, such as temperature prediction [ 22 ] and stock prediction [ 23 ], which have achieved good results. Given the strong correlation between COVID-19 and time, many researchers have used deep learning models such as RNN, LSTM, BILSTM, CNN, GRU, and some hybrid models [ 24 , 25 , 26 , 27 , 28 , 29 ] to predict COVID-19 cases.…”

Section: Introductionmentioning

confidence: 99%

Prediction of COVID-19 Using a WOA-BILSTM Model

Yang

2023

Bioengineering

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The COVID-19 pandemic has had a significant impact on the world, highlighting the importance of the accurate prediction of infection numbers. Given that the transmission of SARS-CoV-2 is influenced by temporal and spatial factors, numerous researchers have employed neural networks to address this issue. Accordingly, we propose a whale optimization algorithm–bidirectional long short-term memory (WOA-BILSTM) model for predicting cumulative confirmed cases. In the model, we initially input regional epidemic data, including cumulative confirmed, cured, and death cases, as well as existing cases and daily confirmed, cured, and death cases. Subsequently, we utilized the BILSTM as the base model and incorporated WOA to optimize the specific parameters. Our experiments employed epidemic data from Beijing, Guangdong, and Chongqing in China. We then compared our model with LSTM, BILSTM, GRU, CNN, CNN-LSTM, RNN-GRU, DES, ARIMA, linear, Lasso, and SVM models. The outcomes demonstrated that our model outperformed these alternatives and retained the highest accuracy in complex scenarios. In addition, we also used Bayesian and grid search algorithms to optimize the BILSTM model. The results showed that the WOA model converged fast and found the optimal solution more easily. Thus, our model can assist governments in developing more effective control measures.

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Temperature Prediction using Machine Learning Approaches

Cited by 24 publications

References 9 publications

Subseasonal Prediction of Summer Temperature in West Africa Using Artificial Intelligence: A Case Study of Senegal

Subseasonal Prediction of Summer Temperature in West Africa Using Artificial Intelligence: A Case Study of Senegal

Temperature data of Hyderabad from the temperature of three neighboring cities using the ANN and the multiple regression methods

Prediction of COVID-19 Using a WOA-BILSTM Model

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