Application of ARIMA and XGBoost to analyze and predict China’s coastal capture production

Wu Yuchen; Xu Hailong

doi:10.12284/hyxb2025130

Volume 47 Issue 11

Nov. 2025

Turn off MathJax

Article Contents

Article Navigation > Haiyang Xuebao > 2025 > 47(11): 131-140

Wu Yuchen，Xu Hailong. Application of ARIMA and XGBoost to analyze and predict China’s coastal capture production[J]. Haiyang Xuebao，2025, 47(11)：131–140 doi: 10.12284/hyxb2025130

Citation:

Wu Yuchen，Xu Hailong. Application of ARIMA and XGBoost to analyze and predict China’s coastal capture production[J]. Haiyang Xuebao，2025, 47(11)：131–140 doi: 10.12284/hyxb2025130

Citation:

Wu Yuchen，Xu Hailong. Application of ARIMA and XGBoost to analyze and predict China’s coastal capture production[J]. Haiyang Xuebao，2025, 47(11)：131–140 doi: 10.12284/hyxb2025130

PDF( 1292 KB)

Application of ARIMA and XGBoost to analyze and predict China’s coastal capture production

doi: 10.12284/hyxb2025130

Wu Yuchen^1
,,
Xu Hailong^{1, 2, 3
,
,}

1.
Department of Fishery Sciences, Tianjin Agricultural University, Tianjin 300384, China
2.
Tianin Key Laboratory of Aqua-Ecology and Aquaculture National Demonstration Center for Experimental Aqua-Ecology, Tianjin 300384, China
3.
Fisheries Ecological Quantitative Assessment and Management Laboratory (Tianin Agriculture University), Tianjin 300384, China

Received Date: 2025-08-06
Rev Recd Date: 2025-09-18

Available Online: 2025-10-11

Publish Date: 2025-11-30

Abstract

Abstract

To comprehensively understand and analyze the current status and developmental trends of capture resource utilization in China’s coastal waters, this study constructs individual ARIMA models and their hybrid models with XGBoost, using fishery production data from 1980, 1985, 1991, 1997, 2003, 2009, and 2016 as temporal reference points. The model demonstrating the highest fitting accuracy was selected to forecast the marine catch in China's coastal waters from 2024 to 2028. The results indicate that the individual ARIMA models exhibited Mean Absolute Percentage Errors (MAPE) ranging from 0.11% to 12.12% and coefficients of determination (R²) between 0.4561 and 0.9794, while the hybrid models showed MAPE values of 0.12%−12.12% and R² values of 0.7584−0.9933. Notably, the ARIMA(1, 2, 1) hybrid model constructed with 1980 capture data demonstrated optimal fitting performance, with MAPE and R² values of 0.12% and 0.9933, respectively. This model forecasts a marine catch of 9.5×10⁶ t in China’s coastal waters from 2024 to 2028, indicating a gradual upward trend. The research findings reveal that the predictive accuracy of both model types initially decreases and subsequently increases with the extension of time series data, achieving optimal prediction accuracy and fitting degree with the longest time series. The hybrid model demonstrates significantly superior predictive accuracy compared to the individual ARIMA model. The forecasted catch values for 2024−2028 indicate an increase of less than 0.1%.
- different time series,
- catch volume,
- ARIMA model,
- XGBoost model,
- prediction

FullText(HTML)

References(31)

References

[1]	金显仕, 田洪林, 单秀娟. 我国近海渔业资源研究历程及展望[J]. 水产学报, 2023, 47(11): 119310. Jin Xianshi, Tian Honglin, Shan Xiujuan. Development and prospects of studies on inshore fisheries resources in China[J]. Journal of Fisheries of China, 2023, 47(11): 119310.
[2]	粮农组织. 2022年世界渔业和水产养殖状况: 努力实现蓝色转型[R]. 罗马: 联合国粮食及农业组织. 2022. FAO. The State of World Fisheries and Aquaculture 2022: Towards Blue Transformation[R]. Rome: FAO. 2022.
[3]	Costello C, Ovando D. Status, institutions, and prospects for global capture fisheries[J]. Annual Review of Environment and Resources, 2019, 44: 177−200. doi: 10.1146/annurev-environ-101718-033310
[4]	Froese R, Zeller D, Kleisner K, et al. What catch data can tell us about the status of global fisheries[J]. Marine Biology, 2012, 159(6): 1283−1292. doi: 10.1007/s00227-012-1909-6
[5]	韩杨. 1949年以来中国海洋渔业资源治理与政策调整[J]. 中国农村经济, 2018(9): 14−28. Han Yang. Marine fishery resources management and policy adjustment in China since 1949[J]. Chinese Rural Economy, 2018(9): 14−28.
[6]	刘子飞. 我国近海捕捞渔业管理政策困境、逻辑与取向[J]. 生态经济, 2018, 34(11): 47−53. Liu Zifei. Research on management policies of marine capture fishery resources: issues, logic and choice[J]. Ecological Economy, 2018, 34(11): 47−53.
[7]	丁依婷, 胡志远, 董帝渤. 基于深度学习和时空特征融合的海洋渔船密度预测方法[J]. 应用海洋学学报, 2024, 43(2): 350−359. Ding Yiting, Hu Zhiyuan, Dong Dibo. A density prediction method for fishing vessel based on deep learning and fusion of spatial-temporal features[J]. Journal of Applied Oceanography, 2024, 43(2): 350−359.
[8]	张忠, 陈新军, 余为. 基于灰色理论系统的西非海域捕捞渔获量预测[J]. 上海海洋大学学报, 2023, 32(4): 818−828. Zhang Zhong, Chen Xinjun, Yu Wei. Catch prediction off the coast of West Africa based on grey theory system[J]. Journal of Shanghai Ocean University, 2023, 32(4): 818−828.
[9]	黄冬梅, 高静霞. 基于SDSS的渔业资源预测决策系统[J]. 计算机工程, 2010, 36(6): 270−272,275. Huang Dongmei, Gao Jingxia. Fishery resources forecasting decision system based on SDSS[J]. Computer Engineering, 2010, 36(6): 270−272,275.
[10]	李辉华, 郭弘艺, 唐文乔, 等. ARIMA模型在预测长江靖江段沿岸鱼类渔获量时间格局中的应用[J]. 水产学报, 2008, 32(6): 899−905. Li Huihua, Guo Hongyi, Tang Wenqiao, et al. ARIMA model application to predict temporal pattern of fish catches of coastal area at Jingjiang Reach of the Yangtze River[J]. Journal of Fisheries of China, 2008, 32(6): 899−905.
[11]	董江水, 诸英富. 时间序列分析模型在江苏省河蟹总产量预测中的应用[J]. 金陵科技学院学报, 2008, 24(3): 102−105. Dong Jiangshui, Zhu Yingfu. Application of time series analysis model on total yield of Chinese mitten-handed crab in Jiangsu Province[J]. Journal of Jinling Institute of Technology, 2008, 24(3): 102−105.
[12]	程炎宏, 樊伟. 东海区海洋捕捞产量的时间序列分析研究[J]. 中国水产科学, 2001, 8(3): 31−34. Cheng Yanhong, Fan Wei. Study of time-serial analysis of marine capture yield in East China Sea region[J]. Journal of Fishery Sciences of China, 2001, 8(3): 31−34.
[13]	蔡格菁, 傅海彬, 蒋仁斌, 等. 基于ARIMA模型的渔业经济预测及其优化[J]. 计算机与现代化, 2019(4): 87−91. Cai Gejing, Fu Haibin, Jiang Renbin, et al. ARIMA based fishing economy prediction model and its optimization[J]. Computer and Modernization, 2019(4): 87−91.
[14]	宋大德, 汪金涛, 陈新军, 等. 时间序列分析模型在黄海南部小黄鱼资源量预测中的应用[J]. 海洋学报, 2020, 42(12): 26−33. Song Dade, Wang Jintao, Chen Xinjun, et al. Application of time series analysis model on stock prediction of small yellow croaker (Larimichthys polyactis) in the southern Yellow Sea[J]. Haiyang Xuebao, 2020, 42(12): 26−33.
[15]	王啸, 刘文俊, 张健. 基于ARIMA的海洋尼诺指数对中西太平洋黄鳍金枪鱼年际CPUE的影响[J]. 南方水产科学, 2023, 19(4): 10−20. Wang Xiao, Liu Wenjun, Zhang Jian. Effect of Oceanic Niño index on interannual CPUE of yellowfin tuna (Thunnus albacares) in Western and Central Pacific Ocean based on ARIMA model[J]. South China Fisheries Science, 2023, 19(4): 10−20.
[16]	李辉华, 郭弘艺, 唐文乔, 等. 长江下游靖江段沿岸贝氏䱗渔获量的时间格局及ARIMA模型预测[J]. 中国水产科学, 2009, 16(3): 357−364. Li Huihua, Guo Hongyi, Tang Wenqiao, et al. Temporal pattern of Hemiculter bleekeri catches at Jingjiang Section of the Yangtze River and catch forecast by using ARIMA model[J]. Journal of Fishery Sciences of China, 2009, 16(3): 357−364.
[17]	Soykan C U, Eguchi T, Kohin S, et al. Prediction of fishing effort distributions using boosted regression trees[J]. Ecological Applications, 2014, 24(1): 71−83. doi: 10.1890/12-0826.1
[18]	韩秋影, 黄小平, 施平. 生态补偿在海洋生态资源管理中的应用[J]. 生态学杂志, 2007, 26(1): 126−130. Han Qiuying, Huang Xiaoping, Shi Ping. Ecological compensation and its application in marine ecological resources management[J]. Chinese Journal of Ecology, 2007, 26(1): 126−130.
[19]	黄硕琳, 唐议. 渔业管理理论与中国实践的回顾与展望[J]. 水产学报, 2019, 43(1): 211−231. Huang Shuolin, Tang Yi. Review and prospect of theories of fisheries management and China’s practice[J]. Journal of Fisheries of China, 2019, 43(1): 211−231.
[20]	Bai Jushan, Perron P. Estimating and testing linear models with multiple structural changes[J]. Econometrica, 1998, 66(1): 47−78. doi: 10.2307/2998540
[21]	Vaihola S, Kininmonth S. Ecosystem management policy implications based on Tonga main tuna species catch data 2002-2018[J]. Diversity, 2023, 15(10): 1042. doi: 10.3390/d15101042
[22]	徐海龙, 韩颖, 谷德贤, 等. 渔业资源自然死亡估算方法研究进展[J]. 水产科技情报, 2019, 46(3): 160−164,171. Xu Hailong, Han Ying, Gu Dexian, et al. Methods of estimated natural mortality in fishery resources assessment[J]. Fisheries Science & Technology Information, 2019, 46(3): 160−164,171.
[23]	King K B S, Giacomini H C, Wehrly K, et al. Using historical catch data to evaluate predicted changes in fish relative abundance in response to a warming climate[J]. Ecography, 2023, 2023(8): e06798. doi: 10.1111/ecog.06798
[24]	Aldrin M, Aanes F L, Tvete I F, et al. Caveats with estimating natural mortality rates in stock assessment models using age aggregated catch data and abundance indices[J]. Fisheries Research, 2021, 243: 106071. doi: 10.1016/j.fishres.2021.106071
[25]	李琪, 刘淑德, 王琨, 等. 渔获量时间序列长度对基于CMSY方法的资源评估结果的影响[J]. 海洋学报, 2023, 45(3): 27−39. Li Qi, Liu Shude, Wang Kun, et al. Effects of lengths of catch time series on stock assessment using CMSY method[J]. Haiyang Xuebao, 2023, 45(3): 27−39.
[26]	Miyagawa M, Ichinokawa M, Yoda M, et al. Commentary: stock status assessments for 12 exploited fishery species in the Tsushima Warm Current region, Southwest Japan and East China, using the CMSY and BSM methods[J]. Frontiers in Marine Science, 2021, 8: 703039. doi: 10.3389/fmars.2021.703039
[27]	Ju Peilong, Tian Yongjun, Chen Mingru, et al. Evaluating stock status of 16 commercial fish species in the coastal and offshore waters of Taiwan using the CMSY and BSM methods[J]. Frontiers in Marine Science, 2020, 7: 618. doi: 10.3389/fmars.2020.00618
[28]	Bouch P, Minto C, Reid D G. Comparative performance of data-poor CMSY and data-moderate SPiCT stock assessment methods when applied to data-rich, real-world stocks[J]. ICES Journal of Marine Science, 2021, 78(1): 264−276. doi: 10.1093/icesjms/fsaa220
[29]	Zhai Lu, Liang Cui, Pauly D. Assessments of 16 exploited fish stocks in Chinese waters using the CMSY and BSM methods[J]. Frontiers in Marine Science, 2020, 7: 483993. doi: 10.3389/fmars.2020.483993
[30]	周淑婷, 邹晓荣, 邹邦郁. 基于改进的XGBoost模型的大西洋大眼金枪鱼渔场预报[J]. 大连海洋大学学报, 2025, 40(3): 481−488. Zhou Shuting, Zou Xiaorong, Zou Bangyu. Prediction of bigeye tuna (Thunnus obesus) fishing ground in the Atlantic Ocean based on the modified XGBoost model[J]. Journal of Dalian Ocean University, 2025, 40(3): 481−488.
[31]	袁红春, 高子玥, 张天蛟. 基于改进的XGBoost模型预测南太平洋长鳍金枪鱼资源丰度[J]. 海洋湖沼通报, 2022, 44(2): 112−120. Yuan Hongchun, Gao Ziyue, Zhang Tianjiao. Prediction of albacore tuna abundance insouth Pacific based on improved XGBoost model[J]. Transactions of Oceanology and Limnology, 2022, 44(2): 112−120.