Abstract
The projection pursuit model is an important tool for processing high-dimensional nonnormal and nonlinear data, and it has a wide range of applications. In this paper, clustering analysis and information entropy theory are simultaneously introduced into the projection pursuit model. The K-means clustering method is used to cluster high-dimensional data, and information entropy is used to measure the overall dispersion and local aggregation of projection data. A projection pursuit model based on clustering and information entropy is proposed. The new model has both the classification advantages of clustering analysis and the evaluation advantages of the projection pursuit model. In the case test, different cases are tested by using the Shapiro-Wilk test, Kolmogorov-Smirnov test, Epps-Pulley test, etc. The results show that in most cases, the new model is better than the original model, and the advantage is clearer when the data dimension is higher. Finally, the new model is applied to the agricultural drought risk assessment of Qiqihar, a major grain-producing area in China that is prone to drought. The regional agricultural drought risk shows a downward trend over time. Spatially, the risk of the central and southern regions is low, while the risk of the northern and western regions is high. Regional ability to resist disasters is the main reason for the spatial and temporal differences. This paper extends projection pursuit model theory, analyzes the characteristics of the spatiotemporal variation in regional agricultural drought risk, and provides a reference for identifying the hidden dangers of drought disasters and disaster reduction.
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References
An H, Yang WJ, Huang J, Huang AI, Wan ZC, An M (2020) Identify and assess hydropower project’s multidimensional social impacts with rough set and projection pursuit model. Complexity 2020:16. https://doi.org/10.1155/2020/9394639
Blauhut V, Stahl K, Stagge JH, Tallaksen LM, De Stefano L, Vogt J (2016) Estimating drought risk across Europe from reported drought impacts, hazard indicators and vulnerability factors. Hydrol Earth Syst Sci 20:2779–2800. https://doi.org/10.5194/hess-20-2779-2016
Carrão H, Naumann G, Barbosa P (2016) Mapping global patterns of drought risk: An empirical framework based on sub-national estimates of hazard, exposure and vulnerability. Global Environ Change 108–124. https://doi.org/10.1016/j.gloenvcha.2016.04.012
Chen HL, Wu YY, Liu XL, Zhang WJ (2020) Competitiveness evaluation of chinese dairy industry based on accelerated genetic algorithm projection pursuit model. Math Probl Eng 2020:17. https://doi.org/10.1155/2020/6185234
Chen JF, Deng MH, Xia L, Wang HM (2017) Risk assessment of drought, based on IDM-VFS in the Nanpan River Basin, Yunnan Province, China. Sustainability 9:16. https://doi.org/10.3390/su9071124
Cui HY, Zhao Y, Chen YN, Zhang X, Wang XQ, Lu Q, Jia LM, Wei ZM (2017) Assessment of phytotoxicity grade during composting based on EEM/PARAFAC combined with projection pursuit regression. J Hazard Mater 326:10–17. https://doi.org/10.1016/j.jhazmat.2016.09.059
Cui Y, Jiang SM, Jin JL, Feng P, Ning SW (2019) Decision-making of irrigation scheme for soybeans in the Huaibei plain based on grey entropy weight and grey relation-projection pursuit. Entropy 21:25. https://doi.org/10.3390/e21090877
Dai M, Huang SZ, Huang Q, Leng GY, Guo Y, Wang L, Fang W, Li P, Zheng XD (2020) Assessing agricultural drought risk and its dynamic evolution characteristics. Agric Water Manag 231:12. https://doi.org/10.1016/j.agwat.2020.106003
Epps TW, Pulley LB (1983) A test for normality based on the empirical characteristic function. Biometrika 70:723–726. https://doi.org/10.1007/BF02613322
Eskandari E, Mohammadzadeh H, Nassery H, Vadiati M, Zadeh AM, Kisi O (2022) Delineation of isotopic and hydrochemical evolution of karstic aquifers with different cluster-based (HCA, KM, FCM and GKM) methods. J Hydrol 609:14. https://doi.org/10.1016/j.jhydrol.2022.127706
Friedman JH, Tukey JW (1974) A projection pursuit algorithm for exploratory data analysis. IEEE Trans Comput 100:881–890. https://doi.org/10.1109/T-C.1974.224051
Hagenlocher M, Meza I, Anderson CC, Min A, Renaud FG, Walz Y, Siebert S, Sebesvari Z (2019) Drought vulnerability and risk assessments: state of the art, persistent gaps, and research agenda. Environ Res Lett 14:13. https://doi.org/10.1088/1748-9326/ab225d
Hall P (1989) On polynomial-based projection indices for exploratory projection pursuit. Ann Stat 17:589–605. https://doi.org/10.1214/aos/1176347127
Hao HH, Zhu HY, Wang FQ (2022) Regional agricultural drought risk assessment based on attribute interval identification: a study from Zhengzhou, China. Water Supply 22:5309–5330. https://doi.org/10.2166/ws.2022.177
Hoque MAA, Pradhan B, Ahmed N, Sohel MSI (2021) Agricultural drought risk assessment of Northern New South Wales, Australia using geospatial techniques. Sci Total Environ 756:15. https://doi.org/10.1016/j.scitotenv.2020.143600
Ke X, Shi W, Yang C, Guo H, Mougharbel A (2022) Ecological security evaluation and spatial-temporal evolution characteristics of natural resources based on wind-driven optimization algorithm. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-022-03967-6
Kundu A, Dutta D, Patel N, Denis D, Chattoraj K (2021) evaluation of socio-economic drought risk over Bundelkhand Region of India using analytic hierarchy process (AHP) and geo-spatial techniques. J Indian Soc Remote Sens 49:1365–1377. https://doi.org/10.1007/s12524-021-01306-9
Le T, Sun C, Choy S, Kuleshov Y (2021) Regional drought risk assessment in the Central Highlands and the South of Vietnam. Geomat Nat Hazards Risk 12:3140–3159. https://doi.org/10.1080/19475705.2021.1998232
Liu D, Liu CL, Fu Q, Li TX, Khan MI, Cui S, Faiz MA (2018) Projection pursuit evaluation model of regional surface water environment based on improved chicken swarm optimization algorithm. Water Resour Manag 32:1325–1342. https://doi.org/10.1007/s11269-017-1872-6
Luo D, Ye LL, Sun DC (2020) Risk evaluation of agricultural drought disaster using a grey cloud clustering model in Henan province. China Int J Disaster Risk Reduct 49:11. https://doi.org/10.1016/j.ijdrr.2020.101759
Maasoumi E, Racine J (2002) Entropy and predictability of stock market returns. J Econom 107:291–312. https://doi.org/10.1016/S0304-4076(01)00125-7
Murthy CS, Laxman B, Sai MVRS (2015) Geospatial analysis of agricultural drought vulnerability using a composite index based on exposure, sensitivity and adaptive capacity. Int J Disaster Risk Reduct 12:163–171. https://doi.org/10.1016/j.ijdrr.2015.01.004
Nemati A, Najafabadi SHG, Joodaki G, Nadoushani SSM (2020) Spatiotemporal drought characterization using gravity recovery and climate experiment (grace) in the central Plateau Catchment of Iran. Environ Process 7:135–157. https://doi.org/10.1007/s40710-019-00413-7
Ouyang XH, Wang JB, Chen X, Zhao XL, Ye H, Watson AE, Wang SQ (2021) Applying a projection pursuit model for evaluation of ecological quality in Jiangxi Province, China. Ecol Indic 133:15. https://doi.org/10.1016/j.ecolind.2021.108414
Palchaudhuri M, Biswas S (2016) Application of AHP with GIS in drought risk assessment for Puruliya district, India. Nat Hazards 84:1905–1920. https://doi.org/10.1007/s11069-016-2526-3
Qin JX, Duan WL, Chen YN, Dukhovny VA, Sorokin D, Li YP, Wang XX (2022) Comprehensive evaluation and sustainable development of water-energy-food-ecology systems in Central Asia. Renew Sust Energ Rev 157:17. https://doi.org/10.1016/j.rser.2021.112061
Roukounis CN, Tsihrintzis VA (2022) Indices of coastal vulnerability to climate change: a review. Environ Process 9:29. https://doi.org/10.1007/s40710-022-00577-9
Shannon CE (1948) A mathematical theory of communication. Acm Sigmob Mob Computi Commun Rev 27:379–423. https://doi.org/10.1002/j.1538-7305.1948.tb00917.x
Shao HY, Sun XF, Lin Y, Xian W, Zhou YZ, Yuan LG, Qi JG (2021) A method for spatio-temporal process assessment of eco-geological environmental security in mining areas using catastrophe theory and projection pursuit model. Prog Phys Geogr Earth Environ 45:647–668. https://doi.org/10.1177/0309133320982542
Shaphiro SS, Wilk MB (1965) An analysis of variance test for normality (complete samples). Biometrika 52:591–611. https://doi.org/10.2307/2333709
Ullah H, Akbar M (2021) Drought risk analysis for water assessment at gauged and ungauged sites in the low rainfall regions of Pakistan. Environ Process 8:139–162. https://doi.org/10.1007/s40710-020-00478-9
Wang B, Li YF, Meng Y, Liu XA (2017) Comprehensive evaluation of integrative drip-irrigation and fertilization using a projection pursuit model based on the improved double chains quantum genetic algorithm. Desalin Water Treat 66:229–234. https://doi.org/10.5004/dwt.2017.20228
Wang JX, Wang ZY, Yang C, Wang NX, Yu XJ (2012) Optimization of the number of components in the mixed model using multi-criteria decision-making. Appl Math Modell 36:4227–4240. https://doi.org/10.1016/j.apm.2011.11.053
Wang MW, Wang Y, Shen FQ, Jin JL (2022) Projection pursuit method based on connection cloud model for assessment of debris flow disasters. J Environ Inform. https://doi.org/10.3808/jei.202200472
Wang Q, Yang X (2020) Investigating the sustainability of renewable energy - An empirical analysis of European Union countries using a hybrid of projection pursuit fuzzy clustering model and accelerated genetic algorithm based on real coding. J Clean Prod 268:26. https://doi.org/10.1016/j.jclepro.2020.121940
Wang Q, Yang X (2021) Evaluating the potential for sustainable development of China’s shale gas industry by combining multi-level DPSIR framework, PPFCI technique and RAGA algorithm. Sci Total Environ 780:16. https://doi.org/10.1016/j.scitotenv.2021.146525
Wijitkosum S (2018) Fuzzy AHP for drought risk assessment in lam Ta Kong watershed, the north-eastern region of Thailand. Soil Water Res 13:218–225. https://doi.org/10.17221/158/2017-SWR
Xiang XJ, Li Q (2020) Water resources vulnerability assessment and adaptive management based on projection pursuit model. J Coast Res 431–435. https://doi.org/10.2112/si103-088.1
Xu HF, Xu KX, Yang YJ (2021) Risk assessment model of agricultural drought disaster based on grey matter-element analysis theory. Nat Hazards 107:2693–2707. https://doi.org/10.1007/s11069-021-04681-1
Yu S, Lu HW (2018) An integrated model of water resources optimization allocation based on projection pursuit model - Grey wolf optimization method in a transboundary river basin. J Hydrol 559:156–165. https://doi.org/10.1016/j.jhydrol.2018.02.033
Yu X, Xie JC, Jiang RG, Zuo GG, Liang J (2020) Assessment of water resource carrying capacity based on the chicken swarm optimization-projection pursuit model. Arabian J Geosci 13:14. https://doi.org/10.1007/s12517-019-5010-z
Yu XH, Xu HY, Wang SQ (2021) vulnerability assessment and spatiotemporal differentiation of provinces tourism economic system based on the projection pursuit clustering model. Discrete Dyn Nat Soc 2021:12. https://doi.org/10.1155/2021/4330728
Zarch MAA (2022) Past and future global drought assessment. Water Resour Manag 36:5259–5276. https://doi.org/10.1007/s11269-022-03304-z
Zarei AR, Moghimi MM, Koohi E (2021) sensitivity assessment to the occurrence of different types of droughts using GIS and AHP techniques. Water Resour Manag 35:3593–3615. https://doi.org/10.1007/s11269-021-02906-3
Zhang L, Li HB (2022) Construction risk assessment of deep foundation pit projects based on the projection pursuit method and improved set pair analysis. Appl Sci Basel. https://doi.org/10.3390/app12041922
Zhang Q, Gu XH, Singh VP, Kong DD, Chen XH (2015) Spatiotemporal behavior of floods and droughts and their impacts on agriculture in China. Glob Planet Chang 131:63–72. https://doi.org/10.1016/j.gloplacha.2015.05.007
Zhang Q, Yu HQ, Sun P, Singh VP, Shi PJ (2019) Multisource data based agricultural drought monitoring and agricultural loss in China. Glob Planet Chang 172:298–306. https://doi.org/10.1016/j.gloplacha.2018.10.017
Zhou RX, Jin JL, Cui Y, Ning SW, Bai X, Zhang LB, Zhou YL, Wu CG, Tong F (2022) Agricultural drought vulnerability assessment and diagnosis based on entropy fuzzy pattern recognition and subtraction set pair potential. Alex Eng J 61:51–63. https://doi.org/10.1016/j.aej.2021.04.090
Zhu YN, Li L, Zhao Y, Liang ZM, Li HH, Wang LZ, Wang QM (2018) Regional comprehensive drought disaster risk dynamic evaluation based on projection pursuit clustering. Water Policy 20:410–428. https://doi.org/10.2166/wp.2018.010
Zou BP, Xu ZP, Wang JX, Luo ZY, Hu LS (2021) Evaluation of the total quality of tunnel contour using projection pursuit dynamic cluster method. Adv Civ Eng 2021:17. https://doi.org/10.1155/2021/6660719
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This research was supported by funds from the National Natural Science Foundation of China (No. 52009019).
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Wei Pei (W Pei): conceptualization, methodology, software, and writing; Lei Hao (L Hao): software and data curation; Qiang Fu (Q Fu): project administration and funding acquisition; Yongtai Ren (YT Ren): formal analysis; Tianxiao Li (TX Li): data curation.
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Pei, W., Hao, L., Fu, Q. et al. Study on Agricultural Drought Risk Assessment Based on Information Entropy and a Cluster Projection Pursuit Model. Water Resour Manage 37, 619–638 (2023). https://doi.org/10.1007/s11269-022-03391-y
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DOI: https://doi.org/10.1007/s11269-022-03391-y