Weather Prediction Using LSTM Neural Networks

Srivastava, Anitej; Anto, S.

doi:10.1109/i2ct54291.2022.9824268

Cited by 10 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The input gate controls the flow of information into the cell, the forget gate determines which information to discard, and the output gate controls the flow of information out of the cell. LSTM has been successfully applied in various tasks, such as speech recognition (Oruh et al 2022), image captioning (Wang et al 2016), and weather forecasting (Srivastava & S 2022). The mathematical definition of LSTM is as the following:…”

Section: Long Short-term Memorymentioning

confidence: 99%

Identifying Flare-indicative Photospheric Magnetic Field Parameters from Multivariate Time-series Data of Solar Active Regions

Alshammari,

Hamdi,

Filali Boubrahimi

2024

ApJS

View full text Add to dashboard Cite

Photospheric magnetic field parameters are frequently used to analyze and predict solar events. Observation of these parameters over time, i.e., representing solar events by multivariate time-series (MVTS) data, can determine relationships between magnetic field states in active regions and extreme solar events, e.g., solar flares. We can improve our understanding of these events by selecting the most relevant parameters that give the highest predictive performance. In this study, we propose a two-step incremental feature selection method for MVTS data using a deep-learning model based on long short-term memory (LSTM) networks. First, each MVTS feature (magnetic field parameter) is evaluated individually by a univariate sequence classifier utilizing an LSTM network. Then, the top performing features are combined to produce input for an LSTM-based multivariate sequence classifier. Finally, we tested the discrimination ability of the selected features by training downstream classifiers, e.g., Minimally Random Convolutional Kernel Transform and support vector machine. We performed our experiments using a benchmark data set for flare prediction known as Space Weather Analytics for Solar Flares. We compared our proposed method with three other baseline feature selection methods and demonstrated that our method selects more discriminatory features compared to other methods. Due to the imbalanced nature of the data, primarily caused by the rarity of minority flare classes (e.g., the X and M classes), we used the true skill statistic as the evaluation metric. Finally, we reported the set of photospheric magnetic field parameters that give the highest discrimination performance in predicting flare classes.

show abstract

Section: Long Short-term Memorymentioning

confidence: 99%

Identifying Flare-indicative Photospheric Magnetic Field Parameters from Multivariate Time-series Data of Solar Active Regions

Alshammari,

Hamdi,

Filali Boubrahimi

2024

ApJS

View full text Add to dashboard Cite

show abstract

“…ANNs, SVM, fuzzy time series for forecasting, and RNNs are a few techniques. The long-term weather parameter prediction, which LSTMs [4] have shown to be optimal, is a significant problem. Like ARIMA, the Prophet model is an additive model that offers automatic hyper-tuning parameter selection.…”

Section: International Journal Of Research In Science and Technologymentioning

confidence: 99%

Using Additive and Deep Learning Algorithms for Weather Forecasting

Kakarla

2024

IJRST

View full text Add to dashboard Cite

One significant element that directly impacts agricultural activities is the weather. The type of crop that should be grown depends significantly on the temperature and humidity of the area. Therefore, farmers need to be aware of upcoming weather trends to plan their farming operations. Although there are several traditional weather prediction services, they are always run by governments and rely on intricate modelling. In this scenario, time series forecasting becomes an effective method of predicting these temperature patterns because it needs historical weather data and very little computer power to provide results. In this work, we examine the steps involved in putting both additive and regression-based models into practice and evaluate their respective performances to determine which strategy is most effective for weather prediction.

show abstract

“…Among the forecasting approaches, there are ANNs, SVM, Fuzzy time series for forecasting and RNNs. A major challenge is the prediction of weather parameters over a long duration for which LSTMs [4] have proven to be ideal. The Prophet model is also an additive model like ARIMA and provides automatic hyper-tuning parameter selection.…”

Section: Modelsmentioning

confidence: 99%

An Approach to Weather Forecasting using Additive and Deep Learning Algorithms

Balikondwar,

Moon

2024

IJRASET

View full text Add to dashboard Cite

Weather is an important factor that directly affects farming activities. The temperature and humidity of a region play a crucial role in the type of crop to be cultivated. Thus, It becomes important for farmers to know the future trends in the weather to plan farming activities. There are several classical weather prediction services but all of them depend on complex modelling and are operated by government authorities. In this case, time series becomes an effective way of forecasting these temperature trends as it not only requires minimal computational resources but only the past weather data to achieve results. In this paper, we study the process of implementing both additive and regression-based models and compare their performance to decide which approach is the best for weather prediction.

show abstract

Weather Prediction Using LSTM Neural Networks

Cited by 10 publications

References 6 publications

Identifying Flare-indicative Photospheric Magnetic Field Parameters from Multivariate Time-series Data of Solar Active Regions

Identifying Flare-indicative Photospheric Magnetic Field Parameters from Multivariate Time-series Data of Solar Active Regions

Using Additive and Deep Learning Algorithms for Weather Forecasting

An Approach to Weather Forecasting using Additive and Deep Learning Algorithms

Contact Info

Product

Resources

About