Agricultural Science and Food Processing Home About Editors Current Issue
-
CiteScore
0.10
Impact Factor
  1. Home
  2. Journals
  3. Agricultural Science and Food Processing
  4. Volume 2, Issue 1
Research Progress on Time Series Prediction of Agricultural Disaster Risk: A Mini-review
Review Article | Published: 19 November 2024
Agricultural Science and Food Processing Volume 2, Issue 1, 2025: 1-11
Volume 2, Issue 1, Agricultural Science and Food Processing
Volume 2, Issue 1, 2025
Submit Manuscript Edit a Special Issue
Academic Editor
Bin Guo
Bin Guo
Northwest University, China
Article QR Code
Article QR Code
Scan the QR code for reading
Popular articles
Research on A Ship Trajectory Classification Method Based on Deep Learning YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data Bridging Modalities: A Survey of Cross-Modal Image-Text Retrieval Visual Feature Extraction and Tracking Method Based on Corner Flow Detection Inaugural Editorial of the Chinese Journal of Information Fusion Simultaneous Spatiotemporal Bias Compensation and Data Fusion for Asynchronous Multisensor Systems YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform
Agricultural Science and Food Processing, Volume 2, Issue 1, 2025: 1-11

Open Access | Review Article | 19 November 2024
Research Progress on Time Series Prediction of Agricultural Disaster Risk: A Mini-review
by
Feng Fang Feng Fang 1
Jingjing Lin Jingjing Lin 1 *
Jing Wang Jing Wang 2 *
Peixuan Chen Peixuan Chen 1
Pengcheng Huang Pengcheng Huang 1
Xing Wang Xing Wang 1
Yulong Ma Yulong Ma 1
Liwei Liu Liwei Liu 1
Dawei Wang Dawei Wang 3
1 Lanzhou Regional Climate Center, Lanzhou 730020, China
2 Lanzhou Institute of Arid Meteorology, Lanzhou 730020,China
3 College of Earth Science and Engineering, West Yunnan University of Applied Sciences, Dali 671006, China
* Corresponding Authors: Jingjing Lin, [email protected] ; Jing Wang, [email protected]
DOI: 10.62762/ASFP.2024.610643
Received: 16 October 2024, Accepted: 09 November 2024, Published: 19 November 2024  
   PDF  (630.42 KB) Full-Text HTML XML
Article Metrics Cite This Article
Abstract
Food security is crucial for human survival and national economic development, but frequent meteorological disasters have caused great harm to agricultural production. Therefore, it is very important and meaningful to study how to quickly and accurately predict the loss rate of disasters. Only based on historical loss sequence, the time series prediction method can effectively predict future loss. Therefore, this paper first briefly describes the main means of time series prediction, namely statistical methods and machine learning algorithms. Secondly, the commonly used machine learning algorithms for disaster loss time series prediction, and its application cases and existing problems, were introduced in detail. To address the issue of small sample sizes for loss predication, data augmentation techniques can be used; To address the issue of data non-stationarity, Empirical Mode Decomposition (EMD) can be used to decompose the original sequence into relatively stationary sub-sequences. In addition, exploratory solutions have been proposed, such as ensemble learning strategies for multiple machine learners, and combining machine learning algorithms with optimization algorithms, strong prediction strategies, or attention mechanisms. Finally, a summary solution for conventional disaster damage prediction was proposed.
Keywords
food security
meteorological disasters
time series prediction
machine learning
Data Availability Statement
Not applicable.
Funding
This work was supported without any funding.
Conflicts of Interest
Feng Fang, Jingjing Lin, Peixuan Chen, Pengcheng Huang, Xing Wang, Yulong Ma, Liwei Liu, and Xiaowei Wang are employees of the Lanzhou Regional Climate Center, Lanzhou 730020, China. Jing Wang is an employee of the Lanzhou Institute of Arid Meteorology, Lanzhou 730020, China.
Ethical Approval and Consent to Participate
Not applicable.
References
  1. Yuan, S., Wang, G., Chen, J., & Guo, W. (2019). Assessing the forecasting of comprehensive loss incurred by typhoons: a combined PCA and BP neural network model. Journal of Artificial Intelligence, 1(2), 69.
    [Google Scholar]
  2. Sun, H., Wang, J., & Ye, W. (2021). A data augmentation-based evaluation system for regional direct economic losses of storm surge disasters. International journal of environmental research and public health, 18(6), 2918.
    [CrossRef]   [Google Scholar]
  3. Young, C. C., & Liu, W. C. (2015). Prediction and modelling of rainfall–runoff during typhoon events using a physically-based and artificial neural network hybrid model. Hydrological Sciences Journal, 60(12), 2102-2116.
    [CrossRef]   [Google Scholar]
  4. Jin, X., Shi, X., Gao, J., Xu, T., & Yin, K. (2018). Evaluation of loss due to storm surge disasters in China based on econometric model groups. International journal of environmental research and public health, 15(4), 604.
    [CrossRef]   [Google Scholar]
  5. Gao, S., Huang, Y., Zhang, S., Han, J., Wang, G., Zhang, M., & Lin, Q. (2020). Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation. Journal of Hydrology, 589, 125188.
    [CrossRef]   [Google Scholar]
  6. Chai, T., & Xue, H. (2021). A study on ship collision conflict prediction in the Taiwan Strait using the EMD-based LSSVM method. PLoS one, 16(5), e0250948.
    [CrossRef]   [Google Scholar]
  7. Du, X., Li, X., Zhang, S., Zhao, T., Hou, Q., Jin, X., & Zhang, J. (2022). High-accuracy estimation method of typhoon storm surge disaster loss under small sample conditions by information diffusion model coupled with machine learning models. International Journal of Disaster Risk Reduction, 82, 103307.
    [CrossRef]   [Google Scholar]
  8. Guo, H., Yin, K., & Huang, C. (2022). Modeling of Direct Economic Losses of Storm Surge Disasters Based on a Novel Hybrid Forecasting System. Frontiers in Marine Science, 8, 804541.
    [CrossRef]   [Google Scholar]
  9. Yang, W., Wang, J., Niu, T., & Du, P. (2019). A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting. Applied energy, 235, 1205-1225.
    [CrossRef]   [Google Scholar]
  10. Kappi, M., & Mallikarjuna, B. (2024). Artificial intelligence and machine learning for disaster prediction: a scientometric analysis of highly cited papers. Natural Hazards, 1-21.
    [Google Scholar]
  11. Wu, H., & Wang, J. (2021). A method for prediction of waterlogging economic losses in a subway station project. Mathematics, 9(12), 1421.
    [CrossRef]   [Google Scholar]
  12. Jin, C. Z., Jia, L. J., Yang, Z. J., & Wada, K. (2002, December). On convergence of a BCLS algorithm for noisy autoregressive process estimation. In Proceedings of the 41st IEEE Conference on Decision and Control, 2002. (Vol. 4, pp. 4252-4257). IEEE.
    [CrossRef]   [Google Scholar]
  13. Jin, Z., Shu, Y., Liu, J., & Yang, O. W. (2000, May). Prediction-based network bandwidth control. In 2000 Canadian Conference on Electrical and Computer Engineering. Conference Proceedings. Navigating to a New Era (Cat. No. 00TH8492) (Vol. 2, pp. 675-679). IEEE.
    [CrossRef]   [Google Scholar]
  14. Qin, M., Li, Z., & Du, Z. (2017). Red tide time series forecasting by combining ARIMA and deep belief network. Knowledge-Based Systems, 125, 39-52.
    [CrossRef]   [Google Scholar]
  15. Zhao, X., Li, H., Ding, L., Wang, W., & Xue, Y. (2020). Forecasting direct economic losses of marine disasters in China based on a novel combined model. International Journal of Disaster Risk Reduction, 51, 101921.
    [CrossRef]   [Google Scholar]
  16. Zhang, S., Zhang, J., Li, X., Du, X., Zhao, T., Hou, Q., & Jin, X. (2022). Estimating the grade of storm surge disaster loss in coastal areas of China via machine learning algorithms. Ecological Indicators, 136, 108533.
    [CrossRef]   [Google Scholar]
  17. Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society, 54(8), 799-805.
    [CrossRef]   [Google Scholar]
  18. Smyl, S. (2020). A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International journal of forecasting, 36(1), 75-85.
    [CrossRef]   [Google Scholar]
  19. Chu, F. L. (2004). Forecasting tourism demand: a cubic polynomial approach. Tourism management, 25(2), 209-218.
    [CrossRef]   [Google Scholar]
  20. Wang, Q., & Song, X. (2019). Forecasting China’s oil consumption: a comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM. Energy, 183, 160-171.
    [CrossRef]   [Google Scholar]
  21. Yi, A., Yang, M., & Li, Y. (2021). Macroeconomic uncertainty and crude oil futures volatility–evidence from China crude oil futures market. Frontiers in Environmental Science, 9, 636903.
    [CrossRef]   [Google Scholar]
  22. Graves, A., Mohamed, A. R., & Hinton, G. (2013, May). Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 6645-6649). IEEE.
    [CrossRef]   [Google Scholar]
  23. Shirzadi, A., Shahabi, H., Chapi, K., Bui, D. T., Pham, B. T., Shahedi, K., & Ahmad, B. B. (2017). A comparative study between popular statistical and machine learning methods for simulating volume of landslides. Catena, 157, 213-226.
    [CrossRef]   [Google Scholar]
  24. Chang, T. K., Talei, A., Alaghmand, S., & Ooi, M. P. L. (2017). Choice of rainfall inputs for event-based rainfall-runoff modeling in a catchment with multiple rainfall stations using data-driven techniques. Journal of Hydrology, 545, 100-108.
    [CrossRef]   [Google Scholar]
  25. Mosavi, A., Ozturk, P., & Chau, K. W. (2018). Flood prediction using machine learning models: Literature review. Water, 10(11), 1536.
    [CrossRef]   [Google Scholar]
  26. Tayfur, G., & Singh, V. P. (2006). ANN and fuzzy logic models for simulating event-based rainfall-runoff. Journal of hydraulic engineering, 132(12), 1321-1330.
    [CrossRef]   [Google Scholar]
  27. Chua, L. H., Wong, T. S., & Sriramula, L. K. (2008). Comparison between kinematic wave and artificial neural network models in event-based runoff simulation for an overland plane. Journal of hydrology, 357(3-4), 337-348.
    [CrossRef]   [Google Scholar]
  28. Chen, C. S., Chen, B. P. T., Chou, F. N. F., & Yang, C. C. (2010). Development and application of a decision group Back-Propagation Neural Network for flood forecasting. Journal of hydrology, 385(1-4), 173-182.
    [CrossRef]   [Google Scholar]
  29. Kan, G., Yao, C., Li, Q., Li, Z., Yu, Z., Liu, Z., ... & Liang, K. (2015). Improving event-based rainfall-runoff simulation using an ensemble artificial neural network based hybrid data-driven model. Stochastic environmental research and risk assessment, 29, 1345-1370.
    [Google Scholar]
  30. Velo-Suárez, L., & Gutiérrez-Estrada, J. C. (2007). Artificial neural network approaches to one-step weekly prediction of Dinophysis acuminata blooms in Huelva (Western Andalucía, Spain). Harmful Algae, 6(3), 361-371.
    [CrossRef]   [Google Scholar]
  31. Zhao, X., Li, H., Ding, L., & Liu, M. (2019). Research and application of a hybrid system based on interpolation for forecasting direct economic losses of marine disasters. International Journal of Disaster Risk Reduction, 37, 101121.
    [CrossRef]   [Google Scholar]
  32. Gu, S. M., Sun, X. H., Wu, Y. H., & Cui, Z. D. (2012, May). An approach to forecast red tide using generalized regression neural network. In 2012 8th International Conference on Natural Computation (pp. 194-198). IEEE.
    [CrossRef]   [Google Scholar]
  33. Shu, X., Zhang, L., Qi, G. J., Liu, W., & Tang, J. (2021). Spatiotemporal co-attention recurrent neural networks for human-skeleton motion prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(6), 3300-3315.
    [CrossRef]   [Google Scholar]
  34. Ding, Y., Zhu, Y., Feng, J., Zhang, P., & Cheng, Z. (2020). Interpretable spatio-temporal attention LSTM model for flood forecasting. Neurocomputing, 403, 348-359.
    [CrossRef]   [Google Scholar]
  35. Onan, A. (2022). Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification. Journal of King Saud University-Computer and Information Sciences, 34(5), 2098-2117.
    [CrossRef]   [Google Scholar]
  36. Wei, X., Zhang, L., Yang, H. Q., Zhang, L., & Yao, Y. P. (2021). Machine learning for pore-water pressure time-series prediction: Application of recurrent neural networks. Geoscience Frontiers, 12(1), 453-467.
    [CrossRef]   [Google Scholar]
  37. Zhang, Y. L., Bai, Y. T., Jin, X. B., Su, T. L., Kong, J. L., & Zheng, W. Z. (2023). Deep Fusion Prediction Method for Nonstationary Time Series Based on Feature Augmentation and Extraction. Applied Sciences, 13(8), 5088.
    [CrossRef]   [Google Scholar]
  38. Sztobryn, M. (2003). Forecast of storm surge by means of artificial neural network. Journal of Sea Research, 49(4), 317-322.
    [CrossRef]   [Google Scholar]
  39. Bray, M., & Han, D. (2004). Identification of support vector machines for runoff modelling. Journal of Hydroinformatics, 6(4), 265-280.
    [CrossRef]   [Google Scholar]
  40. Yu, P. S., Chen, S. T., & Chang, I. F. (2006). Support vector regression for real-time flood stage forecasting. Journal of hydrology, 328(3-4), 704-716.
    [CrossRef]   [Google Scholar]
  41. Chen, S. T., & Yu, P. S. (2007). Pruning of support vector networks on flood forecasting. Journal of hydrology, 347(1-2), 67-78.
    [CrossRef]   [Google Scholar]
  42. Lin, G. F., Chen, G. R., Huang, P. Y., & Chou, Y. C. (2009). Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods. Journal of hydrology, 372(1-4), 17-29.
    [CrossRef]   [Google Scholar]
  43. Gan, H. X., Zhang, B. G., Zheng, Y. Z., & Peng, J. M. (2008). Application of the grey model theory to forecast maritime traffic accident. Ship & Ocean Engineering, 37(6), 99-102.
    [Google Scholar]
  44. Chen, H. S., & Wei, Q. (2013). Application of the Grey Verhulst Model in Predicting Water Traffic Accidents. Journal of Navigation, 36(2), 67–69.
    [Google Scholar]
  45. Zhao, J. N., & Wu, Z. L. (2005). Forecasting of maritime accidents by grey-Markov model. Journal of Dalian Maritime University, 31(4), 15-18.
    [Google Scholar]
  46. Lu, X., & Yang, Y. (2006). Application of regression analysis method in vessel traffic accident forecasting. Journal of Wuhan University of Technology (Transportation Science and Engineering), 30(3), 546-548.
    [Google Scholar]
  47. Wang, Q., & Wang, Z. M. (2013). Forecasting of maritime traffic accidents based on the improved SCGM (1, 1) _c-markov model. Navigation of China, 36(4), 119-124.
    [Google Scholar]
  48. Guo, Z., Dai, X., Li, X., & Ye, S. (2013). Predict typhoon-induced storm surge deviation in a principal component back-propagation neural network model. Chinese Journal of Oceanology and Limnology, 31(1), 219-226.
    [CrossRef]   [Google Scholar]
  49. Cheng, C., Zeng, Q., Zhao, H., Guo, W., & Duan, H. (2019). Social impact assessment of storm surge disaster through dynamic neural network model. International Journal of Performability Engineering, 15(10), 2817.
    [CrossRef]   [Google Scholar]
  50. Dahal, O. (2020). Mapping and Predicting Community Vulnerability to Hurricane Florence in Coastal North Carolina Using Machine Learning (Doctoral dissertation).
    [Google Scholar]
  51. Behera, M. D., Prakash, J., Paramanik, S., Mudi, S., Dash, J., Varghese, R., ... & Srivastava, P. K. (2022). Assessment of tropical cyclone amphan affected inundation areas using sentinel-1 satellite data. Tropical Ecology, 1-11.
    [Google Scholar]
  52. Hien, N. T., Tran, C. T., Nguyen, X. H., Kim, S., Phai, V. D., Thuy, N. B., & Van Manh, N. (2020). Genetic programming for storm surge forecasting. Ocean Engineering, 215, 107812.
    [CrossRef]   [Google Scholar]
  53. Sahana, M., Rehman, S., Sajjad, H., & Hong, H. (2020). Exploring effectiveness of frequency ratio and support vector machine models in storm surge flood susceptibility assessment: A study of Sundarban Biosphere Reserve, India. Catena, 189, 104450.
    [CrossRef]   [Google Scholar]
  54. De Oliveira, M. M., Ebecken, N. F. F., De Oliveira, J. L. F., & de Azevedo Santos, I. (2009). Neural network model to predict a storm surge. Journal of applied Meteorology and Climatology, 48(1), 143-155.
    [CrossRef]   [Google Scholar]
  55. Feng, L., & Zhang, J. (2014). Application of artificial neural networks in tendency forecasting of economic growth. Economic Modelling, 40, 76-80.
    [CrossRef]   [Google Scholar]
  56. Milačić, L., Jović, S., Vujović, T., & Miljković, J. (2017). Application of artificial neural network with extreme learning machine for economic growth estimation. Physica A: Statistical Mechanics and its Applications, 465, 285-288.
    [CrossRef]   [Google Scholar]
  57. Zahedi, J., & Rounaghi, M. M. (2015). Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange. Physica A: Statistical Mechanics and its Applications, 438, 178-187.
    [CrossRef]   [Google Scholar]
  58. Göçken, M., Özçalıcı, M., Boru, A., & Dosdoğru, A. T. (2016). Integrating metaheuristics and artificial neural networks for improved stock price prediction. Expert Systems with Applications, 44, 320-331.
    [CrossRef]   [Google Scholar]
  59. Panda, C., & Narasimhan, V. (2007). Forecasting exchange rate better with artificial neural network. Journal of Policy Modeling, 29(2), 227-236.
    [CrossRef]   [Google Scholar]
  60. Xiong, Z. (2011). Research on RMB exchange rate forecasting model based on combining ARIMA with Neural Networks. The journal of quantitative & technical economics, 6, 65-76.
    [Google Scholar]
  61. Tsai, C. P., & Lee, T. L. (1999). Back-propagation neural network in tidal-level forecasting. Journal of Waterway, Port, Coastal, and Ocean Engineering, 125(4), 195-202.
    [CrossRef]   [Google Scholar]
  62. Mandal, S. (2001). Discussion of “Back-Propagation Neural Network in Tidal-Level Forecasting” by S. Mandal. Journal of Waterway, Port, Coastal, and Ocean Engineering, 127(1), 55-55.
    [CrossRef]   [Google Scholar]
  63. Zheng, H., Lin, F., Feng, X., & Chen, Y. (2020). A hybrid deep learning model with attention-based conv-LSTM networks for short-term traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 22(11), 6910-6920.
    [CrossRef]   [Google Scholar]
  64. Tao, F. U., Gong, Q. Z., Wang, D. Z., & Li, Q. I. (2015). Numerical control machine reliability distribution model research based on the ANN model and HPSO algorithm. J. Sichuan Univ. 52(2), 262–268.
    [Google Scholar]
  65. Vapnik, V. (2013). The nature of statistical learning theory. Springer science & business media.
    [Google Scholar]
  66. Suykens, J. A., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural processing letters, 9, 293-300.
    [Google Scholar]
  67. Jena, R., Pradhan, B., Beydoun, G., Alamri, A. M., & Sofyan, H. (2020). Earthquake hazard and risk assessment using machine learning approaches at Palu, Indonesia. Science of the total environment, 749, 141582.
    [CrossRef]   [Google Scholar]
  68. Kim, T., Song, J., & Kwon, O. S. (2020). Pre-and post-earthquake regional loss assessment using deep learning. Earthquake Engineering & Structural Dynamics, 49(7), 657-678.
    [CrossRef]   [Google Scholar]
  69. Pulinets, S., Krankowski, A., Hernandez-Pajares, M., Marra, S., Cherniak, I., Zakharenkova, I., ... & Budnikov, P. (2021). Ionosphere Sounding for Pre-seismic anomalies identification (INSPIRE): Results of the project and Perspectives for the short-term earthquake forecast. Frontiers in Earth Science, 9, 610193.
    [CrossRef]   [Google Scholar]
  70. Qi, P., & Du, M. (2018). Multi-factor evaluation indicator method for the risk assessment of atmospheric and oceanic hazard group due to the attack of tropical cyclones. International journal of applied earth observation and geoinformation, 68, 1-7.
    [CrossRef]   [Google Scholar]
  71. Sawant, S., Mohite, J., Sakkan, M., & Pappula, S. (2019, July). Near real time crop loss estimation using remote sensing observations. In 2019 8th international conference on Agro-Geoinformatics (Agro-Geoinformatics) (pp. 1-5). IEEE.
    [CrossRef]   [Google Scholar]
  72. Giffard-Roisin, S., Yang, M., Charpiat, G., Kumler Bonfanti, C., Kégl, B., & Monteleoni, C. (2020). Tropical cyclone track forecasting using fused deep learning from aligned reanalysis data. Frontiers in big Data, 3, 1.
    [CrossRef]   [Google Scholar]
  73. Sattarzadeh, A. R., Kutadinata, R. J., Pathirana, P. N., & Huynh, V. T. (2023). A novel hybrid deep learning model with ARIMA Conv-LSTM networks and shuffle attention layer for short-term traffic flow prediction. Transportmetrica A: Transport Science, 1-23.
    [CrossRef]   [Google Scholar]
  74. Tehrany, M. S., Pradhan, B., Mansor, S., & Ahmad, N. (2015). Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena, 125, 91-101.
    [CrossRef]   [Google Scholar]
  75. Ali, S. A., Parvin, F., Pham, Q. B., Vojtek, M., Vojteková, J., Costache, R., ... & Ghorbani, M. A. (2020). GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: a case of Topľa basin, Slovakia. Ecological Indicators, 117, 106620.
    [CrossRef]   [Google Scholar]
  76. Zhi, G., Liao, Z., Tian, W., & Wu, J. (2020). Urban flood risk assessment and analysis with a 3D visualization method coupling the PP-PSO algorithm and building data. Journal of Environmental Management, 268, 110521.
    [CrossRef]   [Google Scholar]
  77. Soltani, K., Ebtehaj, I., Amiri, A., Azari, A., Gharabaghi, B., & Bonakdari, H. (2021). Mapping the spatial and temporal variability of flood susceptibility using remotely sensed normalized difference vegetation index and the forecasted changes in the future. Science of the Total Environment, 770, 145288.
    [CrossRef]   [Google Scholar]
  78. Yin, K., Zhang, Y., & Li, X. (2017). Research on storm-tide disaster losses in China using a new grey relational analysis model with the dispersion of panel data. International journal of environmental research and public health, 14(11), 1330.
    [CrossRef]   [Google Scholar]
  79. Wang, S., Mu, L., Qi, M., Yu, Z., Yao, Z., & Zhao, E. (2021). Quantitative risk assessment of storm surge using GIS techniques and open data: A case study of Daya Bay Zone, China. Journal of environmental management, 289, 112514.
    [CrossRef]   [Google Scholar]
  80. Mohajane, M., Costache, R., Karimi, F., Pham, Q. B., Essahlaoui, A., Nguyen, H., ... & Oudija, F. (2021). Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area. Ecological Indicators, 129, 107869.
    [CrossRef]   [Google Scholar]
  81. Feng, Q., & Liu, Q. (2017). Pre-assessment for the loss caused by storm surge based on the SVM-BP neural network. Marine Environmental Science, 36(4), 615-621.
    [Google Scholar]
  82. Zhang, Y., Ge, T., Tian, W., & Liou, Y. A. (2019). Debris flow susceptibility mapping using machine-learning techniques in Shigatse area, China. Remote Sensing, 11(23), 2801.
    [CrossRef]   [Google Scholar]
  83. Lou, W., Chen, H., Shen, X., Sun, K., & Deng, S. (2012). Fine assessment of tropical cyclone disasters based on GIS and SVM in Zhejiang Province, China. Natural hazards, 64(1), 511-529.
    [Google Scholar]
  84. Cao, Y., Yin, K., & Li, X. (2020). Prediction of Direct Economic Loss Caused by Marine Disasters Based on The Improved GM (1, 1) Model. Journal of Grey System, 32(1).
    [Google Scholar]
  85. He, S. H., Yang, B., & Dai, M. S. (2014). Flood disaster loss prediction based on dynamic recurrent neural network. Chin. Econ, 2014, 16-19.
    [Google Scholar]
  86. Ye, L., & Yu, F. (2002). The long-range change and forecast of storm surge disasters in China. Marine Forecasts, 19(1), 89-96.
    [Google Scholar]
  87. Yang, S., Liu, X., & Liu, Q. (2016). A storm surge projection and disaster risk assessment model for China coastal areas. Natural Hazards, 84, 649-667.
    [Google Scholar]
  88. Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Bui, D. T., ... & Ma, J. (2017). A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena, 151, 147-160.
    [CrossRef]   [Google Scholar]
  89. Liu, W., & Xu, Y. (2020). Randomised learning-based hybrid ensemble model for probabilistic forecasting of PV power generation. IET Generation, Transmission & Distribution, 14(24), 5909-5917.
    [CrossRef]   [Google Scholar]
  90. Bravo, J. M., & Ayuso, M. (2021, March). Forecasting the retirement age: A Bayesian model ensemble approach. In World Conference on Information Systems and Technologies (pp. 123-135). Cham: Springer International Publishing.
    [Google Scholar]
  91. Chen, S., Tang, D., Liu, X., & Chunhua, H. (2018). Assessment of tropical cyclone disaster loss in Guangdong Province based on combined model. Geomatics, Natural Hazards and Risk, 9(1), 431-441.
    [CrossRef]   [Google Scholar]
  92. Meng, C., Xu, W., Qiao, Y., Qin, L., Su, P., & Liao, X. (2024). Predicting vulnerability through hybrid modeling combining GAM and XGBoost-A case of affected population vulnerability to tropical cyclone in Hainan Province. International Journal of Disaster Risk Reduction, 111, 104732.
    [CrossRef]   [Google Scholar]
  93. Yang, J., Chen, S., Tang, Y., Lu, P., Lin, S., Duan, Z., & Ou, J. (2024). A Tropical Cyclone Risk Prediction Framework using Flood Susceptibility and Tree-based Machine Learning Models: County-level Direct Economic Loss Prediction in Guangdong Province. International Journal of Disaster Risk Reduction, 104955.
    [CrossRef]   [Google Scholar]
  94. Niyogi, P., Girosi, F., & Poggio, T. (1998). Incorporating prior information in machine learning by creating virtual examples. Proceedings of the IEEE, 86(11), 2196-2209.
    [CrossRef]   [Google Scholar]
  95. He, Y. L., Wang, P. J., Zhang, M. Q., Zhu, Q. X., & Xu, Y. (2018). A novel and effective nonlinear interpolation virtual sample generation method for enhancing energy prediction and analysis on small data problem: A case study of Ethylene industry. Energy, 147, 418-427.
    [CrossRef]   [Google Scholar]
  96. Li, M., Zhang, R., & Liu, K. (2021). A new marine disaster assessment model combining bayesian network with information diffusion. Journal of Marine Science and Engineering, 9(6), 640.
    [CrossRef]   [Google Scholar]
  97. Chongfu, H. (1997). Principle of information diffusion. Fuzzy sets and Systems, 91(1), 69-90.
    [CrossRef]   [Google Scholar]
  98. Rogoza, W. (2019). Method for the prediction of time series using small sets of experimental samples. Applied Mathematics and Computation, 355, 108-122.
    [CrossRef]   [Google Scholar]
  99. DAN, W. R., YU, Y. X., TANG, Y. J., & FAN, M. (2010). Forecasting method for water quality time series of few and abnormal data. Journal of Computer Applications, 30(2), 486.
    [Google Scholar]
  100. Rajesh, R. (2016). Forecasting supply chain resilience performance using grey prediction. Electronic Commerce Research and Applications, 20, 42-58.
    [CrossRef]   [Google Scholar]
  101. Deng, Y., Zhou, X., Shen, J., Xiao, G., Hong, H., Lin, H., ... & Liao, B. Q. (2021). New methods based on back propagation (BP) and radial basis function (RBF) artificial neural networks (ANNs) for predicting the occurrence of haloketones in tap water. Science of The Total Environment, 772, 145534.
    [CrossRef]   [Google Scholar]
  102. Hao, Y., & Tian, C. (2019). A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting. Applied energy, 238, 368-383.
    [CrossRef]   [Google Scholar]
  103. Niu, X., & Wang, J. (2019). A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting. Applied Energy, 241, 519-539.
    [CrossRef]   [Google Scholar]
  104. Şahin, U. (2019). Forecasting of Turkey’s greenhouse gas emissions using linear and nonlinear rolling metabolic grey model based on optimization. Journal of Cleaner Production, 239, 118079.
    [CrossRef]   [Google Scholar]
  105. Wang, D., He, W., & Shi, R. (2019). How to achieve the dual-control targets of China’s CO2 emission reduction in 2030? Future trends and prospective decomposition. Journal of cleaner production, 213, 1251-1263.
    [CrossRef]   [Google Scholar]
  106. Genovese, E., Hallegatte, S., & Dumas, P. (2011). Damage assessment from storm surge to coastal cities: Lessons from the Miami area. Advancing geoinformation science for a changing world, 21-43.
    [Google Scholar]
  107. Helderop, E., & Grubesic, T. H. (2022). Hurricane storm surge: toward a normalized damage index for coastal regions. Natural Hazards, 110(2), 1179-1197.
    [Google Scholar]
  108. Wang, T. T., & Liu, Q. (2018). Prediction of storm surge disaster loss based on BAS-BP model. Mar. Environ. Sci, 37(03), 457-463.
    [Google Scholar]
  109. Liu, D., Niu, D., Wang, H., & Fan, L. (2014). Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm. Renewable energy, 62, 592-597.
    [CrossRef]   [Google Scholar]
Cite This Article
APA Style
Fang, F., Lin, J., Wang, J., Chen, P., Huang, P., Wang, X., Ma, Y., Liu, L., Wang, D., & Wang, X. (2024). Research Progress on Time Series Prediction of Agricultural Disaster Risk: A Mini-review. Agricultural Science and Food Processing, 2(1), 1–11. https://doi.org/10.62762/ASFP.2024.610643
Article Metrics
Citations:

Google Scholar
0

Crossref

0

Scopus

0

Web of Science

0
Article Access Statistics:
Views: 539
PDF Downloads: 103
Publisher's Note
IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
CC BY Copyright © 2024 by the Author(s). Published by Institute of Emerging and Computer Engineers. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Agricultural Science and Food Processing

Agricultural Science and Food Processing

ISSN: 3066-1579 (Online) | ISSN: 3066-1560 (Print)

Email: [email protected]

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Copyright © 2024 Institute of Emerging and Computer Engineers Inc.