Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

Poornima, S.; Pushpalatha, M.

doi:10.3390/atmos10110668

Cited by 153 publications

(56 citation statements)

References 21 publications

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“…In general, these models are based on variables (also known as predictors) that are most likely to influence the outcome [ 26 ]. Predictive models are widely applied in various applications such as weather forecasting [ 27 , 28 , 29 ], Bayesian spam filters [ 30 , 31 , 32 , 33 ], business [ 34 , 35 , 36 , 37 ], and fraud detection [ 38 , 39 , 40 ]. Predictive models typically include a machine learning algorithm that learns certain properties from a training dataset.…”

Section: Methodsmentioning

confidence: 99%

Indirect Recognition of Predefined Human Activities

Gorjani

Proto

Vaňuš

et al. 2020

Sensors

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The work investigates the application of artificial neural networks and logistic regression for the recognition of activities performed by room occupants. KNX (Konnex) standard-based devices were selected for smart home automation and data collection. The obtained data from these devices (Humidity, CO2, temperature) were used in combination with two wearable gadgets to classify specific activities performed by the room occupant. The obtained classifications can benefit the occupant by monitoring the wellbeing of elderly residents and providing optimal air quality and temperature by utilizing heating, ventilation, and air conditioning control. The obtained results yield accurate classification.

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Section: Methodsmentioning

confidence: 99%

Indirect Recognition of Predefined Human Activities

Gorjani

Proto

Vaňuš

et al. 2020

Sensors

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“…In recent years, research has intensified in the analysis of weather prediction models, specifically of rainfall, both in the medium term and in the short term, using various techniques of Machine learning and Deep Learning [26][27][28][29]. There exists a notable trend in the experimentation of models based on neural networks.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Rainfall Prediction Models Using Machine Learning in Islands with Complex Orography: Tenerife Island

2019

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We present a comparative study between predictive monthly rainfall models for islands of complex orography using machine learning techniques. The models have been developed for the island of Tenerife (Canary Islands). Weather forecasting is influenced both by the local geographic characteristics as well as by the time horizon comprised. Accuracy of mid-term rainfall prediction on islands with complex orography is generally low when carried out with atmospheric models. Predictive models based on algorithms such as Random Forest or Extreme Gradient Boosting among others were analyzed. The predictors used in the models include weather predictors measured in two main meteorological stations, reanalysis predictors from the National Oceanic and Atmospheric Administration, and the global predictor North Atlantic Oscillation, all of them obtained over a period of time of more than four decades. When comparing the proposed models, we evaluated accuracy, kappa and interpretability of the model obtained, as well as the relevance of the predictors used. The results show that global predictors such as the North Atlantic Oscillation Index (NAO) have a very low influence, while the local Geopotential Height (GPH) predictor is relatively more important. Machine learning prediction models are a relevant proposition for predicting medium-term precipitation in similar geographical regions.

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“…Some researchers have used the meteorological variables, such as Maximum Temperature, Minimum Temperature, Maximum Relative Humidity, Minimum Relative Humidity, Wind Speed, Sunshine, and so on, to predict the precipitation [29][30][31][32]. Gleason et al designed the time series and case-crossover model to evaluate associations of precipitation and meteorological factors, such as temperature (daily minimum, maximum, and mean), dew point, relative humidity, sea level pressure, and wind speed (daily maximum and mean) [29].…”

Section: Introductionmentioning

confidence: 99%

“…Gleason et al designed the time series and case-crossover model to evaluate associations of precipitation and meteorological factors, such as temperature (daily minimum, maximum, and mean), dew point, relative humidity, sea level pressure, and wind speed (daily maximum and mean) [29]. Poornima et al presented an Intensified Long Short-Term Memory (Intensified LSTM) using Maximum Temperature, Minimum Temperature, Maximum Relative Humidity, Minimum Relative Humidity, Wind Speed, Sunshine, and Evapotranspiration to predict rainfall [30]. Jinglin et al applied deep belief networks in weather precipitation forecasting using atmospheric pressure, sea level pressure, wind direction, wind speed, relative humidity, and precipitation [33].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Precipitation Based on Recurrent Neural Networks in Jingdezhen, Jiangxi Province, China

et al. 2020

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Precipitation is a critical input for hydrologic simulation and prediction, and is widely used for agriculture, water resources management, and prediction of flood and drought, among other activities. Traditional precipitation prediction researches often established one or more probability models of historical data based on the statistical prediction methods and machine learning techniques. However, few studies have been attempted deep learning methods such as the state-of-the-art for Recurrent Neural Networks (RNNs) networks in meteorological sequence time series predictions. We deployed Long Short-Term Memory (LSTM) network models for predicting the precipitation based on meteorological data from 2008 to 2018 in Jingdezhen City. After identifying the correlation between meteorological variables and the precipitation, nine significant input variables were selected to construct the LSTM model. Then, the selected meteorological variables were refined by the relative importance of input variables to reconstruct the LSTM model. Finally, the LSTM model with final selected input variables is used to predict the precipitation and the performance is compared with other classical statistical algorithms and the machine learning algorithms. The experimental results show that the LSTM is suitable for precipitation prediction. The RNN models, combined with meteorological variables, could predict the precipitation accurately in Jingdezhen City and provide sufficient time to prepare strategies against potential related disasters.

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Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

Cited by 153 publications

References 21 publications

Indirect Recognition of Predefined Human Activities

Indirect Recognition of Predefined Human Activities

Comparative Analysis of Rainfall Prediction Models Using Machine Learning in Islands with Complex Orography: Tenerife Island

Prediction of Precipitation Based on Recurrent Neural Networks in Jingdezhen, Jiangxi Province, China

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