Daily air temperature forecasting using LSTM-CNN and GRU-CNN models

Uluocak, Ihsan; Bilgili, Mehmet

doi:10.1007/s11600-023-01241-y

Artykuł - szczegóły

Tytuł artykułu

Daily air temperature forecasting using LSTM-CNN and GRU-CNN models

Autorzy

Uluocak Ihsan , Bilgili Mehmet

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-023-01241-y

Warianty tytułu

Języki publikacji

Abstrakty

Today, air temperature (AT) is the most critical climatic indicator. This indicator accurately defines global warming and climate change, despite the fact that it has effects on different things, including the environment, hydrology, agriculture, and irrigation. Accurate and timely AT forecasting is crucial since it supplies more significant details that can create credibility for future planning. This study proposes innovative hybrid models that integrate a convolutional neural network (CNN) with a long short-term memory (LSTM) neural network and a gated recurrent unit (GRU) to perform one-day ahead AT predictions. For this purpose, the daily AT data obtained from 2012 to 2019 at the Adana and Ankara meteorological stations over Türkiye under Continental and Mediterranean climate conditions are used. The hybrid GRU-CNN and LSTM-CNN models are compared with various traditional statistical and machine-learning models such as feed-forward neural network, adaptive neuro-fuzzy inference system, autoregressive moving average, GRU, CNN, and LSTM. The success of the prediction models is evaluated utilizing various statistical criteria (MAE, RMSE, NSE, and R2) and visual comparisons. The results show that the proposed hybrid GRU-CNN and LSTM-CNN models in one day-ahead AT predictions yield the best results among all models with high accuracy.

Słowa kluczowe

air temperature forecasting hybrid GRU-CNN model hybrid LSTM-CNN model

temperatura powietrza prognozowanie hybrydowy model GRU-CNN hybrydowy model LSTM-CNN

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2024

Tom

Vol. 72, no. 3

Strony

2107--2126

Opis fizyczny

Bibliogr. 53 poz.

Twórcy

autor

Uluocak Ihsan

Mechanical Engineering Department, Ceyhan Engineering Faculty, Cukurova University, 01950 Adana, Turkey

autor

Bilgili Mehmet

mbilgili@cu.edu.tr

Mechanical Engineering Department, Ceyhan Engineering Faculty, Cukurova University, 01950 Adana, Turkey

https://orcid.org/0000-0002-5339-6120

Bibliografia

1. Abdel-Nasser M, Mahmoud K (2019) Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Comput Appl 31:2727-2740
2. Abu-Salih B, Wongthongtham P, Morrison G, Coutinho K, Al-Okaily M, Huneiti A (2022) Short-term renewable energy consumption and generation forecasting: a case study of Western Australia. Heliyon 8(3):e09152
3. Aghelpour P, Mohammadi B, Biazar SM (2019) Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA. Theoret Appl Climatol 138(3-4):1471-1480
4. Akdi Y, Ünlü KD (2021) Periodicity in precipitation and temperature for monthly data of Turkey. Theoret Appl Climatol 143:957-968
5. Alomar MK, Khaleel F, Aljumaily MM, Masood A, Razali SFM, AlSaadi MA, Al-Ansari N, Hameed MM (2022) Data-driven models for atmospheric air temperature forecasting at a continental climate region. PLoS ONE 17(11):e0277079
6. ArunKumar KE, Kalaga DV, Kumar CMS, Kawaji M, Brenza TM (2022) Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends. Alex Eng J 61(10):7585-7603
7. Azari A, Zeynoddin M, Ebtehaj I, Sattar AM, Gharabaghi B, Bonak-dari H (2021) Integrated preprocessing techniques with linear stochastic approaches in groundwater level forecasting. Acta Geophys 69(4):1395-1411
8. Bajirao TS, Elbeltagi A, Kumar M, Pham QB (2022) Applicability of machine learning techniques for multi-time step ahead runoff forecasting. Acta Geophys 70(2):757-776
9. Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control, Revised. Holden-Day, San Francisco
10. Cho K, Van Merriěnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078
11. Cho M, Kim C, Jung K, Jung H (2022) Water level prediction model applying a long short-term memory (lstm)-gated recurrent unit (gru) method for flood prediction. Water 14(14):2221
12. Dariane AB, Behbahani MM (2023) Development of an efficient input selection method for NN based streamflow model. J Appl Water Eng Res 11(1):127-140
13. García-Duarte Sáenz L, Cifuentes Quintero J, Marulanda G (2023) Short-term spatio-temporal forecasting of air temperatures using deep graph convolutional neural networks. Available at SSRN 3988117
14. Gezici K, §engül S (2023) Estimation and analysis of missing temperature data in high altitude and snow-dominated regions using various machine learning methods. Environ Monit Assess 195(4):1-19
15. GISTEMP Team (2023) GISS Surface Temperature Analysis (GIS-TEMP), version 4. NASA Goddard Institute for Space Studies. https://data.giss.nasa.gov/gistemp/
16. Goyal MK, Bharti B, Quilty J, Adamowski J, Pandey A (2014) Modeling of daily pan evaporation in sub-tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS. Expert Syst Appl 41(11):5267-5276
17. Hilaire J, Minx JC, Callaghan MW, Edmonds J, Luderer G, Nemet GF, Rogelj J, del Mar Zamora M (2019) Negative emissions and international climate goals—learning from and about mitigation scenarios. Clim Change 157:189-219
18. Hou J, Wang Y, Zhou J, Tian Q (2022) Prediction of hourly air temperature based on CNN-LSTM. Geomat Nat Hazard Risk 13(1):1962-1986
19. Hou J, Wang Y, Hou B, Zhou J, Tian Q (2023) Spatial simulation and prediction of air temperature based on CNN-LSTM. Appl Artif Intell 37(1):2166235
20. Hua G, Wang S, Xiao M, Hu S (2023) Research on the uplift pressure prediction of concrete dams based on the CNN-GRU model. Water 15(2):319
21. IPCC (2018) Global warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. In: Masson-Delmotte V, Zhai P, Pörtner HO, Roberts D, Skea J, Shukla PR, Pirani A, Moufouma-Okia W, Péan C, Pidcock R, Connors S, Matthews JBR, Chen Y, Zhou X, Gomis MI, Lonnoy E, Maycock T, Tignor M, Waterfield T (eds). Cambridge University Press, Cambridge, New York, p 616. https://doi.org/10.1017/ 9781009157940
22. Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665-685
23. Kabbilawsh P, Sathish Kumar D, Chithra NR (2020) Trend analysis and SARIMA forecasting of mean maximum and mean minimum monthly temperature for the state of Kerala, India. Acta Geophys 68:1161-1174
24. Katipoglu OM (2022) Prediction of missing temperature data using different machine learning methods. Arab J Geosci 15(1):21
25. Kaur J, Parmar KS, Singh S (2023) Autoregressive models in environmental forecasting time series: a theoretical and application review. Environ Sci Pollut Res 30(8):19617-19641
26. Lenssen N, Schmidt G, Hansen J, Menne M, Persin A, Ruedy R, Zyss D (2019) Improvements in the GISTEMP uncertainty model. J Geophys Res Atmos 124(12):6307-6326. https://doi.org/10.1029/ 2018JD029522
27. Loganathan N, Ibrahim Y (2010) Forecasting international tourism demand in Malaysia using Box Jenkins SARIMA application. South Asian J Tour Herit 3(2):50-60
28. Massaoudi M, Chihi I, Sidhom L et al (2021) An effective hybrid NARX-LSTM model for point and interval PV power forecasting. IEEE Access 9:36571-36588
29. Mazarei Behbahani MR, Mazarei A (2023) A new criteria for determining the best decomposition level and filter for wavelet-based data-driven forecasting frameworks-validating using three case studies on the CAMELS dataset. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-023-02531-z
30. Modaresi F, Araghinejad S, Ebrahimi K (2018) A comparative assessment of artificial neural network, generalized regression neural network, least-square support vector regression, and K-nearest neighbor regression for monthly streamflow forecasting in linear and nonlinear conditions. Water Resour Manag 32:243-258
31. Moghaddamnia A, Remesan R, Kashani MH, Mohammadi M, Han D, Piri J (2009) Comparison of LLR, MLP, Elman, NNARX and ANFIS Models—with a case study in solar radiation estimation. J Atmos Solar Terr Phys 71(8-9):975-982
32. Mohammadi B, Mehdizadeh S, Ahmadi F, Lien NTT, Linh NTT, Pham QB (2021) Developing hybrid time series and artificial intelligence models for estimating air temperatures. Stoch Env Res Risk Assess 35:1189-1204
33. Morice CP, Kennedy JJ, Rayner NA, Winn JP, Hogan E, Killick RE et al (2021) An updated assessment of near-surface temperature change from 1850: the HadCRUT5 data set. J Geophys Res Atmos 126:e2019JD032361
34. Ourworldindata, Average Temperature Anomaly Global (2023) https:// ourworldindata. org/grapher/temperature-anomaly?country= ~Global. Accessed 1 April 2023
35. Ozbek A (2023) Deep learning approach for one-hour ahead forecasting of weather data. Energy Sources Part A Recovery Util Environ Effects 45(3):7606-7628
36. Ozbek A, Sekertekin A, Bilgili M, Arslan N (2021) Prediction of 10-min, hourly, and daily atmospheric air temperature: comparison of LSTM, ANFIS-FCM, and ARMA. Arab J Geosci 14:1-16
37. Phyo PP, Byun YC (2021) Hybrid ensemble deep learning-based approach for time series energy prediction. Symmetry 13(10):1942
38. Rajagopalan S, Santoso S (2009) Wind power forecasting and error analysis using the autoregressive moving average modeling. In 2009 IEEE power & energy society general meeting. IEEE, pp 1-6
39. Sahoo BB, Jha R, Singh A, Kumar D (2019) Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting. Acta Geophys 67(5):1471-1481
40. Santos CAG, do Nascimento GR, de Farias CAS, da Silva RM, Mishra M (2023) Short-and long-term streamflow forecasting using wavelet neural networks for complex watersheds: a case study in the Mahanadi River, India. Ecol Inform 73:101945
41. Sekertekin A, Bilgili M, Arslan N, Yildirim A, Celebi K, Ozbek A (2021) Short-term air temperature prediction by adaptive neurofuzzy inference system (ANFIS) and long short-term memory (LSTM) network. Meteorol Atmos Phys 133:943-959
42. Sekula P, Bokwa A, Bochenek B, Zimnoch M (2019) Prediction of air temperature in the Polish Western Carpathian Mountains with the ALADIN-HIRLAM numerical weather prediction system. Atmosphere 10(4):186
43. Sen D, Huseyinoglu MF, Günay ME (2023) Prediction of global temperature anomaly by machine learning based techniques. Neural Comput Appl. https://doi.org/10.1007/s00521-023-08580-3
44. TSMS (2023) Turkish State Meteorological Service. https://www.mgm. gov.tr/
45. (UNEP) United Nations Environment Programme (2021) Emissions Gap Report 2021. Nairobi. ISBN 978-92-807-3890-2
46. WHO (World Health Organization) (2023) Climate change and human health. https://www. who. int/globalchange/global-campaign/ cop21/en/. Accessed May 2023
47. Wu S, Fu F, Wang L, Yang M, Dong S, He Y, Zhang Q, Guo R (2022) Short-term regional temperature prediction based on deep spatial and temporal networks. Atmosphere 13(12):1948
48. Yonar A, Yonar H (2023) Modeling air pollution by integrating ANFIS and metaheuristic algorithms. Model Earth Syst Environ 9:1621-1631
49. Younis R, Zerr S, Ahmadi Z (2022) Multivariate time series analysis: an interpretable CNN-based model. In: 2022 IEEE 9th international conference on data science and advanced analytics (DSAA). IEEE, pp 1-10
50. Zakhrouf M, Bouchelkia H, Stamboul M, Kim S (2020) Novel hybrid approaches based on evolutionary strategy for streamflow forecasting in the Chellif River, Algeria. Acta Geophys 68:167-180
51. Zhang Z, Dong Y (2020) Temperature forecasting via convolutional recurrent neural networks based on time-series data. Complexity 2020:1-8
52. Zhang X, Tan SC, Li G (2014) Development of an ambient air temperature prediction model. Energy Build 73:166-170
53. Zhu S, Piotrowski AP (2020) River/stream water temperature forecasting using artificial intelligence models: a systematic review. Acta Geophys 68:1433-1442

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-ae540b9d-00f0-40ed-86ba-9a7222dbd61e