Weather Forecasting using Machine Learning Techniques

Singh, Sadhna; Kaushik, Mayank; Gupta, Ambuj; Malviya, Anil Kumar

doi:10.2139/ssrn.3350281

Cited by 20 publications

(10 citation statements)

References 3 publications

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“…The decomposed data are used to forecast the water temperature by using a multilayer perceptron neural network. Singh et al [12] investigated machine learning approaches, such as support vector machines, artificial neural networks, and recurrent neural networks in predicting the temperature. The time series model (ARIMA) is improved by using the map-reduce algorithm to adjust the temperature sample points.…”

Section: Literature Surveymentioning

confidence: 99%

Big Data Analytics Using Swarm-Based Long Short-Term Memory for Temperature Forecasting

Patil¹,

Rekha²,

Solanki³

et al. 2022

Computers, Materials &Amp; Continua

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In the past few decades, climatic changes led by environmental pollution, the emittance of greenhouse gases, and the emergence of brown energy utilization have led to global warming. Global warming increases the Earth's temperature, thereby causing severe effects on human and environmental conditions and threatening the livelihoods of millions of people. Global warming issues are the increase in global temperatures that lead to heat strokes and high-temperature-related diseases during the summer, causing the untimely death of thousands of people. To forecast weather conditions, researchers have utilized machine learning algorithms, such as autoregressive integrated moving average, ensemble learning, and long short-term memory network. These techniques have been widely used for the prediction of temperature. In this paper, we present a swarm-based approach called Cauchy particle swarm optimization (CPSO) to find the hyperparameters of the long shortterm memory (LSTM) network. The hyperparameters were determined by minimizing the LSTM validation mean square error rate. The optimized hyperparameters of the LSTM were used to forecast the temperature of Chennai City. The proposed CPSO-LSTM model was tested on the openly available 25-year Chennai temperature dataset. The experimental evaluation on MAT-LABR2020a analyzed the root mean square error rate and mean absolute error to evaluate the forecasted output. The proposed CPSO-LSTM outperforms the traditional LSTM algorithm by reducing its computational time to 25 min under 200 epochs and 150 hidden neurons during training. The proposed hyperparameter-based LSTM can predict the temperature accurately by having a root mean square error (RMSE) value of 0.250 compared with the traditional LSTM of 0.35 RMSE.

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Section: Literature Surveymentioning

confidence: 99%

Big Data Analytics Using Swarm-Based Long Short-Term Memory for Temperature Forecasting

Patil¹,

Rekha²,

Solanki³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…A study by [10] provides a comprehensive report on climate change in the Himalayas and suggests modeling future climate status, especially for the Himalayan region. [11,12] applied Dl techniques such as DNN and RNN for weather forecasting; however, the scope and data used in their investigation were limited. However, significant issues with these studies are rigorous parameter tuning, cross-validation of the model on different data, and computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

CDLSTM: A Novel Model for Climate Change Forecasting

Haq¹

2022

Computers, Materials &Amp; Continua

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Water received in rainfall is a crucial natural resource for agriculture, the hydrological cycle, and municipal purposes. The changing rainfall pattern is an essential aspect of assessing the impact of climate change on water resources planning and management. Climate change affected the entire world, specifically India's fragile Himalayan mountain region, which has high significance due to being a climatic indicator. The water coming from Himalayan rivers is essential for 1.4 billion people living downstream. Earlier studies either modeled temperature or rainfall for the Himalayan area; however, the combined influence of both in a long-term analysis was not performed utilizing Deep Learning (DL). The present investigation attempted to analyze the time series and correlation of temperature (1796-2013) and rainfall changes over the Himalayan states in India. The Climate Deep Long Short-Term Memory (CDLSTM) model was developed and optimized to forecast all Himalayan states' temperature and rainfall values. Facebook's Prophet (FB-Prophet) model was implemented to forecast and assess the performance of the developed CDLSTM model. The performance of both models was assessed based on various performance metrics and shown significantly higher accuracies and low error rates.

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“…With the advances in computer hardware and increased computing memory, storage, and network capabilities [16], the application of machine learning in atmospheric sciences has increased [17]. The applications of such in-depth techniques include climate change prediction [18], and the modeling of hydrological processes [19], especially since 2000.…”

Section: Introductionmentioning

confidence: 99%

K-Means and C4.5 Decision Tree Based Prediction of Long-Term Precipitation Variability in the Poyang Lake Basin, China

Lou

Yang²,

Shi

et al. 2021

Atmosphere

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The machine learning algorithms application in atmospheric sciences along the Earth System Models has the potential of improving prediction, forecast, and reconstruction of missing data. In the current study, a combination of two machine learning techniques namely K-means, and decision tree (C4.5) algorithms, are used to separate observed precipitation into clusters and classified the associated large-scale circulation indices. Observed precipitation from the Chinese Meteorological Agency (CMA) during 1961–2016 for 83 stations in the Poyang Lake basin (PLB) is used. The results from K-Means clusters show two precipitation clusters splitting the PLB precipitation into a northern and southern cluster, with a silhouette coefficient ~0.5. The PLB precipitation leading cluster (C1) contains 48 stations accounting for 58% of the regional station density, while Cluster 2 (C2) covers 35, accounting for 42% of the stations. The interannual variability in precipitation exhibited significant differences for both clusters. The decision tree (C4.5) is employed to explore the large-scale atmospheric indices from National Climate Center (NCC) associated with each cluster during the preceding spring season as a predictor. The C1 precipitation was linked with the location and intensity of subtropical ridgeline position over Northern Africa, whereas the C2 precipitation was suggested to be associated with the Atlantic-European Polar Vortex Area Index. The precipitation anomalies further validated the results of both algorithms. The findings are in accordance with previous studies conducted globally and hence recommend the applications of machine learning techniques in atmospheric science on a sub-regional and sub-seasonal scale. Future studies should explore the dynamics of the K-Means, and C4.5 derived indicators for a better assessment on a regional scale. This research based on machine learning methods may bring a new solution to climate forecast.

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Weather Forecasting using Machine Learning Techniques

Cited by 20 publications

References 3 publications

Big Data Analytics Using Swarm-Based Long Short-Term Memory for Temperature Forecasting

Big Data Analytics Using Swarm-Based Long Short-Term Memory for Temperature Forecasting

CDLSTM: A Novel Model for Climate Change Forecasting

K-Means and C4.5 Decision Tree Based Prediction of Long-Term Precipitation Variability in the Poyang Lake Basin, China

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