Short-term air temperature prediction by adaptive neuro-fuzzy inference system (ANFIS) and long short-term memory (LSTM) network

Şekertekin, Aliihsan; Bilgili, Mehmet; Arslan, Niyazi; Yıldırım, Alper; Çelebi, Kerimcan; Özbek, Arif

doi:10.1007/s00703-021-00791-4

Cited by 29 publications

(11 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Long Short-Term Memory (LSTM) is a variation of RNN with the capability to prevent gradients decaying or exploding. It can fully explore the non-linear relationship between variables and process complex long-term time series dynamic information [17].…”

Section: The Structure Of Two-layer Lstm Model Networkmentioning

confidence: 99%

A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar

Geng

Jiang

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

It is critical to estimate and eliminate the wavelets of ground penetrating radar (GPR), so as to optimally compensate the energy attenuation and phase distortion. This paper presents a new wavelet extraction method based on a two-layer Long Short-Term Memory (LSTM) network. It only uses several random A-scan echoes (i.e., single channel detection echo sequence) to accurately predict the wavelet of any scene. The layered detection scenes with objects buried in different region are set for the 3D Finite-Difference Time-Domain simulator to generate radar echoes as a dataset. Additionally, the simulation echoes of different scenes are used to test the performance of the neural network. Multiple experiments indicate that the trained network can directly predict the wavelets quickly and accurately, although the simulation environment becomes quite different. Moreover, the measured data collected by the Qingdao Radio Research Institute radar and the unmanned aerial vehicle ground penetrating radar are used for test. The predicted wavelets can perfectly offset the original data. Therefore, the presented LSTM network can effectively predict the wavelets and their tailing oscillations for different detection scenes. The LSTM network has obvious advantages compared with other wavelet extraction methods in practical engineering.

show abstract

Section: The Structure Of Two-layer Lstm Model Networkmentioning

confidence: 99%

A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar

Geng

Jiang

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…An LSTM layer gets to know long-term dependencies between time steps of sequence data. Next, the network completes the function with regression and fully connected output layers (Bilgili et al, 2021;Ozbek et al, 2021;Sekertekin et al, 2021).…”

Section: Long-short Term Memory (Lstm) Neural Networkmentioning

confidence: 99%

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

Özbek

Ünal

Bilgili

2022

Theor Appl Climatol

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…It was reported that the performance of the neural network (NN) model was superior to POAMA in precipitation prediction over three regions in Queensland [ 39 – 41 ]. For temperature prediction, A. Sekertekin et al [ 6 ] used the adaptive neuro-fuzzy inference system (ANFIS) and long-short term memory (LSTM) network to predict temperature for both ultra short-term and short term period(hourly and one day ahead). The results showed that the LSTM model was able to efficiently predict the temperature for both the time scales.…”

Section: Introductionmentioning

confidence: 99%

“…The temperature parameter is seen as the most influential parameter out of all meteorological parameters, which reflects the effect of climate change on earth and its surrounding atmosphere. Recently, climate change has caused extreme natural phenomena such as heat waves, severe winters, heavy snowfall, and droughts worldwide, leading to environmental and health crises [2][3][4][5][6]. Air temperature prediction helps meteorologists to know the likelihood of hurricanes and floods in an area [7].…”

Section: Introductionmentioning

confidence: 99%

Data-driven models for atmospheric air temperature forecasting at a continental climate region

et al. 2022

View full text Add to dashboard Cite

Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important information that can be relied on for future planning. In this study, four Data-Driven Approaches, Support Vector Regression (SVR), Regression Tree (RT), Quantile Regression Tree (QRT), ARIMA, Random Forest (RF), and Gradient Boosting Regression (GBR), have been applied to forecast short-, and mid-term air temperature (daily, and weekly) over North America under continental climatic conditions. The time-series data is relatively long (2000 to 2021), 70% of the data are used for model calibration (2000 to 2015), and the rest are used for validation. The autocorrelation and partial autocorrelation functions have been used to select the best input combination for the forecasting models. The quality of predicting models is evaluated using several statistical measures and graphical comparisons. For daily scale, the SVR has generated more accurate estimates than other models, Root Mean Square Error (RMSE = 3.592°C), Correlation Coefficient (R = 0.964), Mean Absolute Error (MAE = 2.745°C), and Thiels’ U-statistics (U = 0.127). Besides, the study found that both RT and SVR performed very well in predicting weekly temperature. This study discovered that the duration of the employed data and its dispersion and volatility from month to month substantially influence the predictive models’ efficacy. Furthermore, the second scenario is conducted using the randomization method to divide the data into training and testing phases. The study found the performance of the models in the second scenario to be much better than the first one, indicating that climate change affects the temperature pattern of the studied station. The findings offered technical support for generating high-resolution daily and weekly temperature forecasts using Data-Driven Methodologies.

show abstract

Short-term air temperature prediction by adaptive neuro-fuzzy inference system (ANFIS) and long short-term memory (LSTM) network

Cited by 29 publications

References 41 publications

A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar

A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar

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

Data-driven models for atmospheric air temperature forecasting at a continental climate region

Contact Info

Product

Resources

About