[32] 黄晶,蔡思琴,庞甜甜,王慧敏.基于主体建模的城市暴雨洪涝灾害预警策略仿真研究,地球信息科学学报, 2024
[31] 黄晶, 吴星妍, 王慧敏, 戴强. 基于知识图谱的暴雨灾害链挖掘与预测研究预测.工程管理科技前沿, 2024
[30] 黄晶, 王子勍, 戴强, 王慧敏. 考虑人群活动的城市场所洪涝灾害风险动态评估——以深圳市为例 [J]. 地理科学, 2024, 44 (04): 711-720. DOI:10.13249/j.cnki.sgs.20221154.
[29] Huang, J.,Pang T., Liu Z., Wang Z., Wang H. Risk Simulation of Urban Rainstorm Flood Disasters Considering Crowd Activities. Systems, 2023, 11(8): 407. doi:10.3390/systems11080407
[28] Wang, T., Wang, H., Wang, Z., Huang, J*. (2023). Dynamic risk assessment of urban flood disasters based on functional area division—A case study in Shenzhen, China. Journal of Environmental Management, 345:118787. https://doi.org/10.1016/j.jenvman.2023.118787
[27] Huang, J., Zhuo, L., She, J., Kang, J., Liu, Z., Wang, H. (2023). Urban Flood Inundation Probability Assessment Based on an Improved Bayesian Model, Natural Hazards Review, 24(4):04023046. https://doi.org/10.1061/NHREFO.NHENG-1726
[26] Huang, J., Xu, Y., Wen, X., Zhu, X., & Herrera-Viedma, E. (2023). Deriving priorities from the fuzzy best-worst method matrix and its applications: A perspective of incomplete reciprocal preference relation. Information Sciences, 634, 761–778. https://doi.org/10.1016/j.ins.2023.03.125
[25] Wang, D., Huang, J., & Xu, Y. (2023). Integrating intuitionistic preferences into the graph model for conflict resolution with applications to an ecological compensation conflict in Taihu Lake basin. Applied Soft Computing, 135, 110036. https://doi.org/10.1016/j.asoc.2023.110036
[24] Huang, J., Yang, Y., Yang, Y., Fang, Z., & Wang, H. (2022). Risk assessment of urban rainstorm flood disaster based on land use/land cover simulation. Hydrological Processes, 36(12). Portico. https://doi.org/10.1002/hyp.14771
[23] 王慧敏,黄晶*,刘高峰,佟金萍,曾庆彬.大数据驱动的城市洪涝灾害风险感知与预警决策研究范式[J].工程管理科技前沿,2022,41(01):35-41.
[22]. Sun, D., Wang, H., Huang, J.*, Zhang, J., & Liu, G. (2022). Urban road waterlogging risk assessment based on the source–pathway–receptor concept in Shenzhen, China. Journal of Flood Risk Management. Portico. https://doi.org/10.1111/jfr3.12873
[21] Sun, D., Wang, H., Lall, U., Huang, J.*, & Liu, G. (2022). Subway travel risk evaluation during flood events based on smart card data. Geomatics, Natural Hazards and Risk, 13(1), 2796–2818. https://doi.org/10.1080/19475705.2022.2134056
[20] Zhang, J., Wang, H., Huang, J.*, Sun, D., & Liu, G. (2022). Evaluation of Urban Flood Resilience Enhancement Strategies—A Case Study in Jingdezhen City under 20-Year Return Period Precipitation Scenario. ISPRS International Journal of Geo-Information, 11(5), 285. https://doi.org/10.3390/ijgi11050285
[19] Zhang, R., Huang, J., Xu, Y., & Herrera-Viedma, E. (2022). Consensus models with aggregation operators for minimum quadratic cost in group decision making. Applied Intelligence, 53(2), 1370–1390. https://doi.org/10.1007/s10489-021-02948-5
[18] Wang, D., Huang, J., Xu, Y., & Wu, N. (2022). Water–Energy–Food nexus evaluation using an inverse approach of the graph model for conflict resolution based on incomplete fuzzy preferences. Applied Soft Computing, 120, 108703. https://doi.org/10.1016/j.asoc.2022.108703
[17] Lu, Y., Xu, Y., Huang, J., Wei, J., & Herrera-Viedma, E. (2022). Social network clustering and consensus-based distrust behaviors management for large-scale group decision-making with incomplete hesitant fuzzy preference relations. Applied Soft Computing, 117, 108373. https://doi.org/10.1016/j.asoc.2021.108373
[16] Liu, Z., Wang, H., Huang, J.*, & Zhuo, L. (2021). Data Mining of Remotely-Sensed Rainfall for a Large-Scale Rain Gauge Network Design. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 12300–12311. https://doi.org/10.1109/jstars.2021.3131157
[15] Huang, S., Wang, H., Xu, Y., She, J., & Huang, J.* (2021). Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network. Land, 10(2), 210. https://doi.org/10.3390/land10020210
[14] Zhu, S., Huang, J., & Xu, Y. (2021). A consensus model for group decision making with self‐confident linguistic preference relations. International Journal of Intelligent Systems, 36(11), 6360–6386. Portico. https://doi.org/10.1002/int.22553
[13] 黄晶*, 付鹏, 许叶军. 基于随机Petri网的多部门协同农业抗旱应急处置流程建模——以内蒙古巴彦淖尔市为例. 系统管理学报, 2021,30(06):1162-1172
[12] Huang, J., Kang, J., Wang, H., Wang, Z., & Qiu, T. (2020). A Novel Approach to Measuring Urban Waterlogging Depth from Images Based on Mask Region-Based Convolutional Neural Network. Sustainability, 12(5), 2149. https://doi.org/10.3390/su12052149
[11] Huang, J., Cao, W., Wang, H., & Wang, Z. (2020). Affect Path to Flood Protective Coping Behaviors Using SEM Based on a Survey in Shenzhen, China. International Journal of Environmental Research and Public Health, 17(3), 940. https://doi.org/10.3390/ijerph17030940
[10] Cao, W., Yang, Y., Huang, J.*, Sun, D., & Liu, G. (2020). Influential Factors Affecting Protective Coping Behaviors of Flood Disaster: A Case Study in Shenzhen, China. International Journal of Environmental Research and Public Health, 17(16), 5945. https://doi.org/10.3390/ijerph17165945
[9] Wang, Z., Huang, J., Wang, H., Kang, J., & Cao, W. (2020). Analysis of Flood Evacuation Process in Vulnerable Community with Mutual Aid Mechanism: An Agent-Based Simulation Framework. International Journal of Environmental Research and Public Health, 17(2), 560. https://doi.org/10.3390/ijerph17020560
[8] 黄晶*,佘靖雯. 三角洲城市群洪涝灾害脆弱性评估及影响因素分析[J]. 河海大学学报(哲学社会科学版),2020,22(06): 39-45+110-1117.
[7] 黄晶*, 佘靖雯, 袁晓梅, 王慧敏. 基于系统动力学的城市洪涝韧性仿真研究—以南京市为例[J].长江流域资源与环境, 2020,29(11): 2519-2529
[6] 宁思雨,黄晶,汪志强,王慧敏*, 基于投入产出法的洪涝灾害间接经济损失评估——以湖北省为例[J].地理科学进展,2020,39(03):420-432.
[5] Wang, Z., Wang, H., Huang, J., Kang, J., & Han, D. (2018). Analysis of the Public Flood Risk Perception in a Flood-Prone City: The Case of Jingdezhen City in China. Water, 10(11), 1577. https://doi.org/10.3390/w10111577
[4] 孙殿臣,王慧敏,黄晶*,等.鄱阳湖流域城市洪涝灾害风险及土地类型调整策略研究-以景德镇市为例[J].长江流域资源与环境, 2018, 27(12):2856-2866
[3] 佟金萍,黄晶,陈军飞. 洪灾应急管理中的府际合作模式研究[J]. 河海大学学报(哲学社会科学版),2015,04:69-74+92
[2] Huang, J., & Han, D. (2014). Meta-analysis of influential factors on crop yield estimation by remote sensing. International Journal of Remote Sensing, 35(6):2267-2295. doi:10.1080/01431161.2014.890761
[1] Huang, J., Wang, H., Dai, Q., Han, D. (2014). Analysis of NDVI data for crop identification and yield estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,7(11) :4374-4384, doi:10.1109/JSTARS.2014.2334332
