Evaluation of Machine Learning Methods Application in Temperature Prediction

Azari, Babak; Hassan, Khairul; Pierce, Joel; Ebrahimi, Saman

doi:10.52547/crpase.8.1.2747

Cited by 15 publications

(6 citation statements)

References 52 publications

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“…However, the recent development in data-driven models like machine learning models has attracted the attention of researchers to apply these advanced tools for various problems related to different engineering areas (Worden et al, 2007;Akhani et al, 2019;Sparks et al, 2020;Lange and Sippel, 2020;Zounemat et al, 2021;Gandomi et al2021;Kashani et al, 2021;Akhani and Pezeshk, 2022;Azari et al, 2022;Ali et al, 2022). Machine learning models were adopted broadly for studying the scour around hydraulic structures because traditional methods like numerical or experimental models require a lot of data, and these models are costly.…”

Section: Figure 1 An Illustration Showing Scouring Around a Bridge Piermentioning

confidence: 99%

Machine Learning Model for Estimation of Local Scour Depth around Cylindrical Bridge Piers

Ahmed¹,

Waheed²,

Ashiq³

et al. 2022

IJCE

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Scour around bridge piers is a well-known threat to bridge stability worldwide. It can cause losses in lives and the economy, especially during floods. Therefore, an artificial intelligence approach called artificial neural network (ANN) was used to predict the scour depth around bridge piers. The ANN model was trained with laboratory data, including pier width, flow velocity, particle diameter, sediment critical velocity, flow depth, and scour depth. The data was divided into 70% for training, 15 for validation, and 15% for testing. Besides, the ANN model was trained using various training algrthins and a single hidden layer with 20 neurons in the hidden layer. The results showed that the ANN model with Bayesian regularization backpropagation training algorithm provides a better predicted scour depth with a correlation coefficient (R) equal to 0. 9692 and 0.926 for training and test stages, respectively. Besides, it showed a low mean squared error (MSE), which was 0.0034 for training and 0.0066 for the test. These results were slightly better than the ANN with Levenberg-Marquardt backpropagation with R training equals 0.9552 (MSE training = 0.0047), and R test equals 0.838 (MSE test = 0.007).On the other hand, the ANN model with a scaled conjugate gradient backpropagation training algorithm showed worse predictions (R training = 0.7407 and R test = 0.6409). Besides, the ANN model shows better outcomes than the linear regression model. Finally, the sensitivity analysis has shown that the pier width is the most crucial parameter for estimating scour depth using the ANN model.

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Section: Figure 1 An Illustration Showing Scouring Around a Bridge Piermentioning

confidence: 99%

Machine Learning Model for Estimation of Local Scour Depth around Cylindrical Bridge Piers

Ahmed¹,

Waheed²,

Ashiq³

et al. 2022

IJCE

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“…Support Vector Regression (SVR) presents another method to identify relationships between input and output data (Quan et al, 2022). The multi-layer perceptron (MLP) or Artificial neural networks (ANNs) have also become valuable tools in meteorology, excelling at tasks like classification and forecasting (Azari et al;Nezhad et al, 2019). Their success lies in their ability to accurately: identify complex patterns in historical weather data, essential for prediction; capture non-linear relationships between diverse weather variables; and find optimal solutions within intricate weather models, ultimately tackling a broad range of ML problems (Gulrez, 2021;Poh, et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Towards A Smart City Concept - Machine Learning Smarts for the Estimation of Future Temperature Rise in Tabuk City

Albalawi

2024

Resourceedings

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In the Middle Eastern peninsula especially in Saudi Arabia, there is a varsity temperature variation among the individual regions. As far as the city of Tabuk is concerned, no study has been conducted, regarding climate change (the temperature rise) in the Tabuk region and its implications on society and for the flagship “Future Smart Cities” concept. In this paper, machine learning algorithms are used to predict the future temperature values in the Tabuk region. The machine learning algorithms were trained on the data collected from the real-time weather radar stations of the region. Different features from the dataset are used for machine learning models to predict the future temperature. These unique features, for example, humidity and pressure, impact the accurate predictability of the temperature. Temperature prediction is modelled as a regression problem due to the nature of the data, therefore, different machine learning regression models were developed, i.e. Artificial Neural Network ANN-based techniques (Multi-layer Perceptron (MLP)), Decision Trees (DT), K-Nearest Neighbours (KNN), and Support Vector Regression (SVR). Encouragingly, the preliminary model evaluations utilizing Mean Absolute Error (MAE) yielded a high accuracy of 90% on the testing dataset. The findings are envisaged to inform decision-makers in the Climate and Weather Ministry of Tabuk City, potentially contributing to the advancement of the city's "Future Smart Cities" concept.

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“…Linear regression (LR) is one of the oldest and most common ML methods used in diferent areas for regression tasks. LR is applicable to single or multiple variable problems [30][31][32]. Decision tree (DT) regression is another common ML method used in the literature to solve regression problems [33,34].…”

Section: Introductionmentioning

confidence: 99%

“…Decision tree (DT) regression is another common ML method used in the literature to solve regression problems [33,34]. Support vector machine (SVM) regression is also a well-known and typical ML method for determining the link between features and targets [32,[35][36][37]. Artifcial neural network (ANN), one of the most popular ML methods, has been used to solve many ML problems in diferent areas [38][39][40][41].…”

Section: Introductionmentioning

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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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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Evaluation of Machine Learning Methods Application in Temperature Prediction

Cited by 15 publications

References 52 publications

Machine Learning Model for Estimation of Local Scour Depth around Cylindrical Bridge Piers

Machine Learning Model for Estimation of Local Scour Depth around Cylindrical Bridge Piers

Towards A Smart City Concept - Machine Learning Smarts for the Estimation of Future Temperature Rise in Tabuk City

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

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