Employing Moving Average Long Short Term Memory for Predicting Rainfall

Caraka, Rezzy Eko; Chen, Rung Ching; Supatmanto, Budi Darmawan; Arnita., .; Tahmid, Muhammad; Toharudin, Toni

doi:10.1109/taai48200.2019.8959856

Cited by 6 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a study with a more complex network, Lohani (2019) forecasted daily precipitation for Punjab, India with LSTMs. Coupling the moving average approach with LSTMs, Caraka et al (2019) presented a model for Winangun, North Sulawesi, Indonesia. Similarly, Atika et al (2019) for 3 gauges in Surabaya, Indonesia and Miao et al (2019) for the Xiangjiang River Basin in South China utilized LSTMs.…”

Section: D Rainfall Forecastingmentioning

confidence: 99%

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

Sit¹,

Demiray²,

Demir³

2022

Preprint

View full text Add to dashboard Cite

With technological enhancements, the volume, velocity, and variety (3Vs) of the raw digital Earth data have increased in recent years. Due to the increased availability of computer resources and the growing popularity of deep learning applications, this data has been a crucial source for data-driven studies that have transformed the fields of climate and earth science. One of the critical data sources is precipitation supporting climate and earth science studies on modeling, forecasting, and preparedness for extreme events (i.e., floods, droughts, pollution transport). In this study, we worked on an extensive review of manuscripts focusing on use of deep learning methods to tackle challenges either to improve the quality or extrapolate (forecast) rainfall datasets. The purpose of this study is to summarize the most recent developments in deep learning approaches for forecasting rainfall or improving rainfall datasets, as well as highlighting issues, shortcomings, and open questions with insightful recommendations for future directions.

show abstract

Section: D Rainfall Forecastingmentioning

confidence: 99%

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

Sit¹,

Demiray²,

Demir³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…GRU and LSTM have the same function, which is to find out whether there is a long-term dependency and to overcome the problem of vanishing and exploding gradient. LSTM does it through three gates, namely a forget gate that controls how much information needs to be removed, an input gate that controls how many cell states need to be stored, and an output gate that controls how many cell states are sent to the next cell have to [61,62].…”

Section: Uploading Normalization and Separation Of The Datamentioning

confidence: 99%

Optimization of air traffic management efficiency based on deep learning enriched by the long short-term memory (LSTM) and extreme learning machine (ELM)

et al. 2021

View full text Add to dashboard Cite

Nowadays this concept has been widely assessed due to its complexity and sensitivity for the beneficiaries, including passengers, airlines, regulatory agencies, and other organizations. To date, various methods (e.g., statistical and fuzzy techniques) and data mining algorithms (e.g., neural network) have been used to solve the issues of air traffic management (ATM) and delay the minimization problems. However, each of these techniques has some disadvantages, such as overlooking the data, computational complexities, and uncertainty. In this paper, to increase the air traffic management accuracy and legitimacy we used the bidirectional long short-term memory (Bi-LSTMs) and extreme learning machines (ELM) to design the structure of a deep learning network method. The Kaggle data set and different performance parameters and statistical criteria have been used in MATLAB to validate the proposed method. Using the proposed method has improved the criteria factors of this study. The proposed method has had a % increase in air traffic management in comparison to other papers. Therefore, it can be said that the proposed method has a much higher air traffic management capacity in comparison to the previous methods.

show abstract

Employing long short-term memory and Facebook prophet model in air temperature forecasting

Toharudin

Pontoh

Caraka

et al. 2021

Communications in Statistics - Simulation and Computation

View full text Add to dashboard Cite

Employing Moving Average Long Short Term Memory for Predicting Rainfall

Cited by 6 publications

References 11 publications

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

Optimization of air traffic management efficiency based on deep learning enriched by the long short-term memory (LSTM) and extreme learning machine (ELM)

Employing long short-term memory and Facebook prophet model in air temperature forecasting

Contact Info

Product

Resources

About