Abstract
The uncertainty of climatic or weather variations makes human adaptation a challenging task. Though lots of techno developments have taken place addressing the need of predictions and forecasting of climatic behavior, the uncertainty of atmospheric and geoprocesses poses a severe challenge to the effectivity of efforts in dealing with disasters. It has become very crucial to understand the future atmospheric uncertainty process and further predict and forecast for analysis of different activities based on geographical locations. Climate parameters are mandatorily required to improve system activities and output analysis of acquired data. For a sustainable adaptation, this is very important on how the uncertainty of variations in climatic parameters is dealt and the decisions based upon appropriate forecasting model are taken. The work has primarily focused on the development of an appropriate model, training the model with massive monthly rainfall and temperature data which can be used to analyze any climatic predictions and provide support to deal with adverse future situations. This model determines the appropriate amount of rainfall and temperature varied during the dry season and helps set a relationship with other input variables like humidity and maximum and minimum temperature. The model has been trained and tested using the obtained data and performed well with better accuracy. The model developed using long short-term memory algorithm of deep learning for predicting rainfall and temperature climatic variables is supported with the study of the main factors affecting monthly climatic change analysis. Monthly precipitation, rainfall intensity, and temperature have been used in the multipurpose prediction model to analyze the monthly and yearly climatic change prediction. The model has also been optimized for precision, accuracy, and computational speed, which are affected by number of iterations, neurons of a hidden layers, epochs, and batch size.
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References
Alotaibi K, Ghumman AR, Haider H, Ghazaw YM, Shafiquzzaman M (2018) Future predictions of rainfall and temperature using GCM and ANN for arid regions: a case study for the Qassim Region, Saudi Arabia. Water 10(9):1260. https://doi.org/10.3390/w10091260
Al-Shabandar R, Jaddoa A, Liatsis P, Hussain AJ (2020) A deep gated recurrent neural network for petroleum production forecasting. Mach Learn Appl 3:100013
Bang S, Bishnoi R, Chauhan AS, Dixit AK, Chawla I (2019) Fuzzy logic-based crop yield prediction using temperature and rainfall parameters predicted through ARMA, SARIMA, and ARMAX models. In 2019 Twelfth International Conference on Contemporary Computing (IC3). IEEE:1–6. https://doi.org/10.1109/IC3.2019.8844901
Bauer S, Linke HJ, Wagner M (2020) Combining industrial and urban water-reuse concepts for increasing the water resources in water-scarce regions. Water Environ Res 92(7):1027–1041. https://doi.org/10.1002/wer.1298
Cheng M, Fang F, Kinouchi T, Navon IM, Pain CC (2020) Long lead-time daily and monthly streamflow forecasting using machine learning methods. J Hydrol 590:125376. https://doi.org/10.1016/j.jhydrol.2020.125376
Das M, Ghosh SK (2019) FB-STEP: a fuzzy Bayesian network based data-driven framework for spatio-temporal prediction of climatological time series data. Expert Syst Appl 117:211–227. https://doi.org/10.1016/j.eswa.2018.08.057
Dikshit A, Pradhan B, Alamri AM (2021) Long lead time drought forecasting using lagged climate variables and a stacked long short-term memory model. Sci Total Environ 755:142638. https://doi.org/10.1016/j.scitotenv.2020.142638
Ding Y, Zhu Y, Feng J, Zhang P, Cheng Z (2020) Interpretable spatio-temporal attention LSTM model for flood forecasting. Neurocomputing 403:348–359. https://doi.org/10.1016/j.neucom.2020.04.110
Fang Z, Wang Y, Peng L, Hong H (2020) Predicting flood susceptibility using long short-term memory (LSTM) neural network model. J Hydrol 594:125734. https://doi.org/10.1016/j.jhydrol.2020.125734
Greff K, Srivastava RK, Koutník J, Steunebrink BR, Schmidhuber J (2017) LSTM: a search space odyssey. IEEE Trans Neural Networks Learn Syst 28(10):2222–2232. https://doi.org/10.1109/TNNLS.2016.2582924
Hartmann H, Snow JA, Stein S, Su B, Zhai J, Jiang T, Krysanova V, Kundzewicz ZW (2016a) Predictors of precipitation for improved water resources management in the Tarim River basin: creating a seasonal forecast model. J Arid Environ 125:31–42. https://doi.org/10.1016/j.jaridenv.2015.09.010
Hartmann H, Snow JA, Su B, Jiang T (2016b) Seasonal predictions of precipitation in the Aksu-Tarim River basin for improved water resources management. Glob Planet Chang 147:86–96. https://doi.org/10.1016/j.gloplacha.2016.10.018
Hua Y, Zhao Z, Li R, Chen X, Liu Z, Zhang H (2019) Deep learning with long short-term memory for time series prediction. IEEE Commun Mag 57(6):114–119. https://doi.org/10.1109/MCOM.2019.1800155
Janardhanan D, Barrett E (2017) CPU workload forecasting of machines in data centers using LSTM recurrent neural networks and ARIMA models. 2017 12th international conference for internet technology and secured transactions (ICITST). IEEE, pp 55–60. https://doi.org/10.23919/ICITST.2017.8356346
Jung DH, Kim HS, Jhin C, Kim HJ, Park SH (2020) Time-serial analysis of deep neural network models for prediction of climatic conditions inside a greenhouse. Comput Electron Agric 173:105402. https://doi.org/10.1016/j.compag.2020.105402
Kao IF, Zhou Y, Chang LC, Chang FJ (2020) Exploring a long short-term memory based encoder-decoder framework for multi-step-ahead flood forecasting. J Hydrol:124631. https://doi.org/10.1016/j.jhydrol.2020.124631
Kratzert F, Klotz D, Brenner C, Schulz K, Herrnegger M (2018) Rainfall-Runoff modelling using Long-Short-Term-Memory (LSTM) networks. Hydrol Earth Syst Sci 22(11):6006–6022. https://doi.org/10.5194/hess-22-6005-2018
Le LM, Nguyen TC, Pham BT, Ly H-B, Le VM, Le T-T (2019) Development and identification of working parameters for a lychee peeling machine combining rollers and a pressing belt. Agri Eng 1:550–566. https://doi.org/10.3390/agriengineering1040040
Liu Y, Pang Z, Karlsson M, Gong S (2020) Anomaly detection based on machine learning in IoT-based vertical plant wall for indoor climate control. Build Environ 183:107212. https://doi.org/10.1016/j.buildenv.2020.107212
Luo P, Sun Y, Wang S, Wang S, Lyu J, Zhou M, Nover D (2020) Historical assessment and future sustainability challenges of Egyptian water resources management. J Clean Prod 263:121154. https://doi.org/10.1016/j.jclepro.2020.121154
Lv N, Liang X, Chen C, Zhou Y, Li J, Wei H, Wang H (2020) A long short-term memory cyclic model with mutual information for hydrology forecasting: a case study in the Xixian basin. Adv Water Resour 141:103622. https://doi.org/10.1016/j.advwatres.2020.103622
Mehrkanoon S (2019) Deep shared representation learning for weather elements forecasting. Knowl Based Syst 179:120–128. https://doi.org/10.1016/j.knosys.2019.05.009
Mislan, Haviluddin, Hardwinarto S, Sumaryono, Aipassa M (2015) Rainfall monthly prediction based on artificial neural network: a case study in Tenggarong Station, East Kalimantan - Indonesia. Procedia Comput Sci 59:142–151. https://doi.org/10.1016/j.procs.2015.07.528
Ni L, Wang D, Singh VP, Wu J, Wang Y, Tao Y, Zhang J (2020) Streamflow and rainfall forecasting by two long short-term memory-based models. Journal of Hydrology 583:124296. https://doi.org/10.1016/j.jhydrol.2019.124296
Parmar A, Mistree K, Sompura M (2017) Machine learning techniques for rainfall prediction: a review. Int Conf Innov Inf Emb Commun Syst 3
Pham BT, Le LM, Le TT, Bui KTT, Le VM, Ly HB, Prakash I (2020) Development of advanced artificial intelligence models for daily rainfall prediction. Atmos Res 237:104845. https://doi.org/10.1016/j.atmosres.2020.104845
Rahardja D (2020) Statistical methodological review for time-series data. Int J Stat Manag Syst 23(8):1445–1461. https://doi.org/10.1080/09720510.2020.1727618
Ren T, Liu X, Niu J, Lei X, Zhang Z (2020) Real-time water level prediction of cascaded channels based on multilayer perception and recurrent neural network. J Hydrol 585:124783. https://doi.org/10.1016/j.jhydrol.2020.124783
Sahai AK, Soman MK, Satyan V (2000) All India summer monsoon rainfall prediction using an artificial neural network. Clim Dyn 16:291–302. https://doi.org/10.1007/s003820050328
Samal KKR, Babu KS, Das SK (2021) Multi-directional temporal convolutional artificial neural network for PM2. 5 forecasting with missing values: A deep learning approach. Urban Climate 36:100800. https://doi.org/10.1016/j.uclim.2021.100800
Shao B et al (2019) Nickel price forecast based on the LSTM neural network optimized by the improved PSO algorithm. Math Probl Eng 2019. https://doi.org/10.1155/2019/1934796
Shen R, Huang A, Li B, Guo J (2019) Construction of a drought monitoring model using deep learning based on multi-source remote sensing data. Int J Appl Earth Obs Geoinf 79:48–57. https://doi.org/10.1016/j.jag.2019.03.006
Shi X et al (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting. Adv Neural Information Process Syst:802–810
Shook J, Gangopadhyay T, Wu L, Ganapathysubramanian B, Sarkar S, Singh AK (2020) Crop yield prediction integrating genotype and weather variables using deep learning. arXiv preprint arXiv:2006.13847. https://doi.org/10.1371/journal.pone.0252402
Siami-Namini S, Tavakoli N, Namin AS (2018, December) A comparison of ARIMA and LSTM in forecasting time series. In: In 2018 17th IEEE international conference on machine learning and applications (ICMLA). IEEE, pp 1394–1401. https://doi.org/10.1109/ICMLA.2018.00227
Song X, Liu Y, Xue L, Wang J, Zhang J, Wang J, Cheng Z (2020) Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model. J Pet Sci Eng 186:106682. https://doi.org/10.1016/j.petrol.2019.106682
Trinh TA (2018) The impact of climate change on agriculture: findings from households in Vietnam. Environ Resour Econ 71:897–921. https://doi.org/10.1007/s10640-017-0189-5
Wang M, Wang H, Wang J, Liu H, Lu R, Duan T, Ma J (2019) A novel model for malaria prediction based on ensemble algorithms. PloS One 14(12):e0226910. https://doi.org/10.1371/journal.pone.0226910
Yamak PT, Yujian L, Gadosey PK (2019) A comparison between arima, lstm, and gru for time series forecasting. In: In Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence, pp 49–55. https://doi.org/10.1145/3377713.3377722
Yoo C, Han D, Im J, Bechtel B (2019) Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images. ISPRS J Photogramm Remote Sens 157:155–170. https://doi.org/10.1016/j.isprsjprs.2019.09.009
Yuan Q, Shen H, Li T, Li Z, Li S, Jiang Y et al (2020) Deep learning in environmental remote sensing: Achievements and challenges. Remote Sens Environ 241:111716. https://doi.org/10.1016/j.rse.2020.111716
Zhang D, Lindholm G, Ratnaweera H (2018) Use long short-term memory to enhance the Internet of things for combined sewer overflow monitoring. J Hydrol 556:409–418. https://doi.org/10.1016/j.jhydrol.2017.11.018
Zhang D, Peng Q, Lin J, Wang D, Liu X, Zhuang J (2019) Simulating reservoir operation using a recurrent neural network algorithm. Water 11(4):865. https://doi.org/10.3390/w11040865
Acknowledgements
I am sincerely thankful to the Department of Indian Meteorological for providing related data for this study.
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All authors are contributed to the study for implementation and execution. The method preparation, data collection, and execution of model by [Surendra Singh Choudhary] and language grammar and methods outputs checked by [Prof. S. K. Ghosh]. All authors read and approved the final manuscript.
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Highlights
• The prediction of climatic rainfall and temperature parameters.
• The areas of applied environment science using deep learning model.
• The model developed using long short-term memory algorithm of deep learning for predicting rainfall and temperature climatic variables is supported with the study of the main factors affecting monthly climatic change analysis.
• Monthly precipitation, rainfall intensity, and temperature have been used in the multipurpose prediction model to analyze the monthly and yearly climatic change prediction.
• The model has also been optimized for precision, accuracy, and computational speed, which are affected by number of iterations, neurons of a hidden layers, epochs, and batch size.
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Choudhary, S.S., Ghosh, S.K. Analysis of rainfall and temperature using deep learning model. Theor Appl Climatol 153, 755–770 (2023). https://doi.org/10.1007/s00704-023-04493-2
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DOI: https://doi.org/10.1007/s00704-023-04493-2