Employing Sequence-to-Sequence Stacked LSTM Autoencoder Architecture to Forecast Indian Weather

Maharatha, Arpita; Das, Ratnakar; Mishra, Jibitesh; Nayak, Soumya Ranjan; Aluvala, Srinivas

doi:10.1016/j.procs.2024.04.214

Procedia Computer Science

2024

DOI: 10.1016/j.procs.2024.04.214

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Employing Sequence-to-Sequence Stacked LSTM Autoencoder Architecture to Forecast Indian Weather

Arpita Maharatha,

Ratnakar Das,

Jibitesh Mishra

et al.

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2024

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Cited by 2 publications

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Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review

Malashin,

Tynchenko,

Gantimurov

et al. 2024

Polymers

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This review explores the application of Long Short-Term Memory (LSTM) networks, a specialized type of recurrent neural network (RNN), in the field of polymeric sciences. LSTM networks have shown notable effectiveness in modeling sequential data and predicting time-series outcomes, which are essential for understanding complex molecular structures and dynamic processes in polymers. This review delves into the use of LSTM models for predicting polymer properties, monitoring polymerization processes, and evaluating the degradation and mechanical performance of polymers. Additionally, it addresses the challenges related to data availability and interpretability. Through various case studies and comparative analyses, the review demonstrates the effectiveness of LSTM networks in different polymer science applications. Future directions are also discussed, with an emphasis on real-time applications and the need for interdisciplinary collaboration. The goal of this review is to connect advanced machine learning (ML) techniques with polymer science, thereby promoting innovation and improving predictive capabilities in the field.

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Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review

Malashin,

Tynchenko,

Gantimurov

et al. 2024

Polymers

View full text Add to dashboard Cite

show abstract

An Ensemble Forecasting Method based on optimized LSTM and GRU for Temperature and Humidity Forecasting

Saleem,

Waseem

et al. 2024

Eng. Technol. Appl. Sci. Res.

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Temperature and humidity predictions play a crucial role in various sectors such as energy management, agriculture, and climate science. Accurate forecasting of these meteorological parameters is essential for optimizing crop yields, managing energy consumption, and effectively mitigating the impact of climate change. In this context, this paper proposes an enhanced ensemble forecasting method for day-ahead temperature and humidity predictions. The proposed method integrates a Long Short-Term Memory (LSTM) network, a Gated Recurrent Unit (GRU), Particle Swarm Optimization (PSO) and Bayesian Model Averaging (BMA). PSO is employed to optimize the parameters of the LSTM and GRU, thereby improving forecasting accuracy. The method is implemented using Python 3.10 with TensorFlow. Additionally, the proposed approach is compared with ensemble-1, LSTM, and GRU models to demonstrate its effectiveness. The simulation results confirm the superior performance of the proposed method over existing competitive approaches.

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Employing Sequence-to-Sequence Stacked LSTM Autoencoder Architecture to Forecast Indian Weather

Cited by 2 publications

References 15 publications

Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review

Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review

An Ensemble Forecasting Method based on optimized LSTM and GRU for Temperature and Humidity Forecasting

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