Prediction of relative humidity based on long short-term memory network

Hutapea, Marlyna Infryanty; Pratiwi, Yolanda Yulianti; Sarkis, Indra M.; Jaya, Indra Kelana; Sinambela, Marzuki

doi:10.1063/5.0003171

Cited by 13 publications

(9 citation statements)

References 8 publications

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“…The study conducted by [3] found that the performance of an autoregressive integrated moving average (ARIMA) model is better than the Long Short-Term Memory (LSTM) Network for the prediction of relative humidity. On contrary, [8] observed that the LSTM network is capable of predicting complex univariate relative humidity time series with robust no-stationarity. However, Least Square Support Vector Machine (LSSVM) and Adaptive Network-Based Fuzzy Inference System (ANFIS) models were used by [2] for prediction of relative humidity in terms of dry bulb temperature and wet bulb depression and found satisfactory.…”

Section: Introductionmentioning

confidence: 93%

“…However, the M5T model did not perform well for the prediction of relative humidity for the S1, S2, and S3 scenarios with R 2 <0.50 at all meteorological stations (Tables 6-8). A previous study conducted by [8] observed that the LSTM model is capable of forecasting complex univariate relative humidity time series. On contrary, [3] suggested that ARIMA can provide a better prediction of relative humidity as compared to LSTM.…”

Section: Performance Evaluation Of M5t Model In Predicting Relative Humiditymentioning

confidence: 99%

“…from 2007 to 2009 was used for testing in both machine learning models for prediction of relative humidity. However, [8] used only two-year data i.e. 2008 to 2009 for training the LSTM model which might not be enough for reliable predictions.…”

Section: Model Setupmentioning

confidence: 99%

“…The relative humidity is a function of temperature and is regarded as a sensitive parameter in the field of science [4]. Relative humidity plays a vital role in plant growth, agricultural and industrial production and in the prevention and control of air pollution [5]; economic stability of a region, water systems and also in managing renewable and solar energy systems [1,6], weather and climate [7,8]. Moreover, it has also an impact on ozone concentration and adaptive thermal comfort [9].…”

Section: Introductionmentioning

confidence: 99%

“…The relative humidity is an important aspect of the hydrological phase [8] and has a role in alpine hydrology, especially, in a cold and dry climate; any change in temperature and humidity causes larger variations in the ablation of glaciers [10]. The warm environment glaciers are subjected to be influenced more by the change in relative humidity.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Prediction of Relative Humidity in a High Elevated Basin of Western Karakoram by Using Different Machine Learning Models

Adnan¹,

Adnan²,

Liu³

et al. 2021

Weather Forecasting

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Accurate and reliable prediction of relative humidity is of great importance in all fields concerning global climate change. The current study has employed Multivariate Adaptive Regression Spline (MARS) and M5 Tree (M5T) models to predict the relative humidity in the Hunza River basin, Pakistan. Both the models provided the best prediction for the input scenario S6 (RHt-1, RHt-2, RHt-3, Tt-1, Tt-2, Tt-3). The statistical analysis displayed that the MARS model provided a better prediction of relative humidity as compared to M5T at all meteorological stations, especially, at Ziarat followed by Khunjerab and Naltar. The values of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) were (5.98%, 5.43%, and 0.808) for Khunjerab; (6.58%, 5.08%, and 0.806) for Naltar; and (5.86%, 4.97%, 0.815) for Ziarat during the testing of MARS model whereas, the values were (6.14%, 5.56%, and 0.772) for Khunjerab; (6.19%, 5.58% and 0.762) for Naltar and (6.08%, 5.46%, 0.783) for Ziarat during the testing of M5T model. Both the models performed slightly better in training as compared to the testing stage. The current study encourages future research to be conducted at high altitude basins for the prediction of other meteorological variables using machine learning tools.

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Section: Introductionmentioning

confidence: 93%

Section: Performance Evaluation Of M5t Model In Predicting Relative Humiditymentioning

confidence: 99%

Section: Model Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prediction of Relative Humidity in a High Elevated Basin of Western Karakoram by Using Different Machine Learning Models

Adnan¹,

Adnan²,

Liu³

et al. 2021

Weather Forecasting

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Daily average relative humidity forecasting with LSTM neural network and ANFIS approaches

Özbek

Ünal

Bilgili

2022

Theor Appl Climatol

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Because hurricanes, droughts, oods, and heat waves are all important factors in measuring environmental changes, they can all result from changes in atmospheric air temperature and relative humidity (RH). Besides, climate, weather, industry, human health, and plant growth are all affected by RH.Accurately and consistently forecasting RH is a challenge due to its non-linear nature. The present study tried to predict one day ahead of RH in determined provinces from different climatic regions of Turkey (Ankara, Erzurum, Samsun, Diyarbakır, Antalya, and Bilecik) using long short-term memory (LSTM) and adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM) based machine learning models. As evaluation criteria, root mean square error (RMSE), mean absolute error (MAE), and correlation coe cient (R) were employed. The outcomes from the forecasting models were also validated using observed data. During the testing stage, the smallest MAE and RMSE values were discovered to be 5.76% and 7.51%, respectively, in Erzurum province, with an R-value of 0.892 when using the LSTM method.Moreover, the smallest MAE and RMSE values were obtained to be 5.95% and 7.67% respectively, in Erzurum province with an R-value of 0.887 using the ANFIS model according to the hourly RH prediction.The results indicate that both the LSTM and ANFIS approaches performed well in daily RH prediction, with the LSTM and ANFIS approaches producing nearly identical results.

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Relative humidity prediction across the Indian subcontinent using Kumaraswamy distribution based non-Gaussian model

Shad,

Sharma,

Narula

2024

Environ Sci Pollut Res

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Prediction of relative humidity based on long short-term memory network

Cited by 13 publications

References 8 publications

Prediction of Relative Humidity in a High Elevated Basin of Western Karakoram by Using Different Machine Learning Models

Prediction of Relative Humidity in a High Elevated Basin of Western Karakoram by Using Different Machine Learning Models

Daily average relative humidity forecasting with LSTM neural network and ANFIS approaches

Relative humidity prediction across the Indian subcontinent using Kumaraswamy distribution based non-Gaussian model

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