Construction of a Climate Early Warning System: Predicting Future Temperatures and Climate Security Using BiLSTM

Authors

  • Jie Yang
  • Zijun Li

DOI:

https://doi.org/10.54097/zscep661

Keywords:

BiLSTM, K-means, SVM-RF, Safety Early Warning

Abstract

In light of the worsening global climate, providing predictive models for surface temperature and energy consumption is crucial for formulating effective climate action strategies. Initially, a Bi-directional Long Short-Term Memory (BiLSTM) network model is established to predict the maximum surface temperatures over the next century, with the Seasonal AutoRegressive Integrated Moving Average (SARIMA) model serving as a benchmark. To assess the risk levels of climate security, the k-means clustering algorithm is utilized to classify the growth rates of carbon dioxide emissions, enabling the construction of a three-tier climate security early warning index. Subsequently, a hybrid classification model based on Support Vector Machine (SVM) and Random Forest (RF) takes the energy consumption growth rates as inputs and the warning indices as outputs to construct a climate security early warning system. The BiLSTM model is employed to predict the energy consumption growth rates for the upcoming decade, and these rates are input into the SVM-RF model to forecast future warning levels. The study demonstrates that the model can effectively predict the maximum surface temperatures and provide a three-tier safety warning system for future climate risk management. The intent of this research is to offer a novel tool for global climate prevention and to deliver practical application value to policymakers in finance, energy, and environmental sectors.

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References

Seinfeld JH. “Insights on Global Warming,” AICHE JOURNAL 2011, vol. 57, no. 12, pp. 3259-3284. DOI: 10. 1002 / aic.12780.

Andrew AM. “Second thoughts on global warming,” KYBERNETES 2011, vol. 40, no. 1-2, pp. 327-329. DOI: 10.1108/03684921111118077.

Trenberth KE, Fasullo JT. “An apparent hiatus in global warming?” EARTHS FUTURE 2013, vol. 1, no. 1, pp. 19-32. DOI: 10.1002/2013EF000165.

Salm L, Nisbett N, Cramer L et al. “How climate change interacts with inequity to affect nutrition.” WILEY INTERDISCIPLINARY REVIEWS-CLIMATE CHANGE 2021, vol. 12, no. 2, pp. e696. DOI: 10.1002/wcc.696.

Yang JC, Huo JM, He JY et al. “A DBULSTM-Adaboost Model for Sea Surface Temperature Prediction.” PEERJ COMPUTER SCIENCE 2022, vol. 8, pp. e1095. DOI: 10. 7717/ peerj-cs.1095.

Liu TY, Lin Y. “Does global warming affect unemployment? International evidence.” ECONOMIC ANALYSIS AND POLICY 2023, vol. 80, pp. 991-1005. DOI: 10.1016/j.eap. 2023. 09.028.

Diffenbaugh NS, Burke M. “Global warming has increased global economic inequality.” PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2019, vol. 116, no. 20, pp. 9808-9813. DOI: 10.1073/pnas.1816020116.

Dhillon RS, von Wuehlisch G. “Mitigation of global warming through renewable biomass.” BIOMASS & BIOENERGY 2013, vol. 48, pp. 75-89. DOI: 10.1016/j. biombioe. 2012. 11. 005.

He Q, Zha C, Song W et al. “Improved Particle Swarm Optimization for Sea Surface Temperature Prediction.” ENERGIES 2020, vol. 13, no. 6, pp. 1369. DOI: 10.3390/ en 13061369.

Zhang XY, Li YQ, Frery AC et al. “Sea Surface Temperature Prediction With Memory Graph Convolutional Networks.” IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022, vol. 19. DOI: 10.1109/LGRS.2021.3097329.

Hao P, Li S, Song JB et al. “Prediction of Sea Surface Temperature in the South China Sea Based on Deep Learning.” REMOTE SENSING 2023, vol. 15, no. 6, pp. 1656. DOI: 10.3390/rs15061656.

Han Y, Sun KQ, Yan JN et al. “The CNN-GRU model with frequency analysis module for sea surface temperature prediction.” SOFT COMPUTING 2023, vol. 27, no. 13, pp. 8711-8720. DOI: 10.1007/s00500-023-08172-2.

Choi HM, Kim MK, Yang HY. “Deep-learning model for sea surface temperature prediction near the Korean Peninsula.” DEEP-SEA RESEARCH PART II-TOPICAL STUDIES IN OCEANOGRAPHY 2023, vol. 208, pp. 105263. DOI: 10.1016/j.dsr2.2023.105263.

Farhangi F, Sadeghi-Niaraki A, Bazargani JS et al. “Time-Series Hourly Sea Surface Temperature Prediction Using Deep Neural Network Models,” JOURNAL OF MARINE SCIENCE AND ENGINEERING 2023, vol. 11, no. 6, pp. 1136. DOI: 10.3390/jmse11061136.

Cui JY, Zhang MY, Song DM et al. “MODIS Land Surface Temperature Product Reconstruction Based on the SSA-BiLSTM Model.” REMOTE SENSING 2022, vol. 14, no. 4, pp. 958. DOI: 10.3390/rs14040958.

Zhang P, Yang Y, Yin ZY. “BiLSTM-Based Soil-Structure Interface Modeling.” INTERNATIONAL JOURNAL OF GEOMECHANICS 2021, vol. 21, no. 7, pp. 04021096. DOI: 10.1061/ (ASCE)GM.1943-5622.0002058.

Xia DW, Yang N, Jian SY et al. “SW-BiLSTM: a Spark-based weighted BiLSTM model for traffic flow forecasting.” MULTIMEDIA TOOLS AND APPLICATIONS 2022, vol. 81, no. 17, pp. 23589-23614. DOI: 10.1007/s11042-022-12039-3.

Lu WJ, Li JZ, Wang JY et al. “A CNN-BiLSTM-AM method for stock price prediction.” NEURAL COMPUTING & APPLICATIONS 2021, vol. 33, no. 10, pp. 4741-4753. DOI: 10.1007/s00521-020-05532-z.

Pecoraro G, Calvello M, Piciullo L et al. “Monitoring strategies for local landslide early warning systems” LANDSLIDES 2019, vol. 16, no. 2, pp. 213-231. DOI: 10. 1007/s10346-018-1068-z.

Quinn AC, Meek T, Waldmann C. “Obstetric early warning systems to prevent bad outcome.” CURRENT OPINION IN ANESTHESIOLOGY 2016, vol. 29, no. 3, pp. 268-272. DOI: 10.1097/ACO.0000000000000338.

Xie XQ, He WP, Gu B et al. “Can kurtosis be an early warning signal for abrupt climate change?” CLIMATE DYNAMICS 2019, vol. 52, no. 11, pp. 6863-6876. DOI: 10.1007/s00382-018-4549-9.

Xie XQ, Mei Y, Gu B et al. “Changing Box-Cox transformation parameter as an early warning signal for abrupt climate change.” CLIMATE DYNAMICS 2023, vol. 60, no. 11-12, pp. 4133-4143. DOI: 10.1007/s00382-022-06563-z.

Zhang Z, Feng BY, Shuai JB et al. “ENSO-climate fluctuation-crop yield early warning system-A case study in Jilin and Liaoning Province in Northeast China.” PHYSICS AND CHEMISTRY OF THE EARTH 2015, vol. 87-88, pp. 10-18. DOI: 10.1016/j.pce.2015.09.015.

Linares C, Martinez GS, Kendrovski V et al. “A new integrative perspective on early warning systems for health in the context of climate change.” ENVIRONMENTAL RESEARCH 2020, vol. 187, pp. 109623. DOI: 10.1016/j. envres. 2020.109623.

Zhu ZY, Liu N. “Early Warning of Financial Risk Based on K-Means Clustering Algorithm.” COMPLEXITY 2021, vol. 2021, pp. 5571683. DOI: 10.1155/2021/5571683.

Seo D, Kim S, Oh S et al. “K-Means Clustering-Based Safety System in Large-Scale Industrial Site Using Industrial Wireless Sensor Networks.” SENSORS 2022, vol. 22, no. 8, pp. 2897. DOI: 10.3390/s22082897.

Silva RP, Zarpelao BB, Cano A et al. “Time Series Segmentation Based on Stationarity Analysis to Improve New Samples Prediction.” SENSORS 2021, vol. 21, no. 21, pp. 7333. DOI: 10.3390/s21217333.

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Published

11-03-2024

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Articles

How to Cite

Yang, J., & Li, Z. (2024). Construction of a Climate Early Warning System: Predicting Future Temperatures and Climate Security Using BiLSTM. Frontiers in Computing and Intelligent Systems, 7(2), 11-20. https://doi.org/10.54097/zscep661