基于组合赋权-云模型的水上机场场址评价方法

翁建军; 刘管江

doi:10.3963/j.jssn.1674-4861.2022.02.015

基于组合赋权-云模型的水上机场场址评价方法

doi: 10.3963/j.jssn.1674-4861.2022.02.015

翁建军^{1, 2, ,},
刘管江¹

1.
武汉理工大学航运学院武汉 430063
2.
武汉理工大学内河航运技术湖北重点实验室武汉 430063

基金项目:

国家自然科学基金项目 51809206

详细信息

通讯作者:
翁建军（1965—）硕士，教授.研究方向：船舶避碰，海事管理理论与技术. E-mail：wjj11233@163.com

中图分类号: U656.1
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出版历程
- 收稿日期: 2022-01-13
- 网络出版日期: 2022-05-18

A Site Evaluation of Water Aerodrome Based on Combined Weighting and a Cloud Model

WENG Jianjun^{1, 2
, ,},
LIU Guanjiang¹

1.
School of Navigation, Wuhan University of Technology, Wuhan 430063, China
2.
Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, China

摘要

摘要: 当水上机场与航道共处于同一片水域时，难免会形成水上飞机与船舶的会遇局面，对水上飞机的起降、滑行安全以及附近水域的船舶航行安全造成影响。为保障水上飞机和船舶的安全，评价水上机场场址的合理性与安全性尤为重要。提出了组合赋权和云模型相结合的水上机场场址评价方法，选取了气象环境、水文环境、通航环境和起降水域空域环境4个一级评价指标和11个二级评价指标，构建了水上机场场址评价指标体系。利用改进层次分析法与熵权法分别求得评价指标的主客观权重，以偏差极小化为目标，引入对策模型计算主客观权重最优的线性组合系数，获得组合权重，结合云模型构建了水上机场场址综合评价模型。以镇江大路水上机场为例进行实例验证，结果表明：镇江大路水上机场场址的评价结果为较好。该机场自建成运营以来未发生安全事故，评价结果与该机场的实际运营情况相符。同时在计算过程中云模型兼顾了数据的随机性和模糊性，可以较好地解决模糊综合评价法在隶属度函数选择时的不确定性问题，进一步增加了评价结果的可靠性。与经典的模糊综合评价法进行对比验证，二者的评价结果一致，验证了该模型的有效性和实用性，该模型可用于拟建水上机场的选址，也可对已建机场的营运安全进行评估分析。
- 交通安全 /
- 水上机场 /
- 场址评价 /
- 组合赋权 /
- 博弈论 /
- 云模型
Abstract: When a water aerodrome and a navigation channel coexist on a same water surface, seaplanes and ships may encounter each other, which may influence the takeoff, landing, and taxiing of seaplanes, and the navigation safety of vessels in nearby waters. To ensure the safety of seaplanes and vessels, it is extremely important to evaluate the rationality and the safety of water aerodrome site. A method for site evaluation of water aerodrome based on a combined weighting method and a cloud model is proposed. An evaluation system is developed in order to appropriately evaluate the appropriateness of the site of water aerodrome, which include four first-level and 11 second-level indicators from the following aspects: meteorological, hydrological, navigable, and airspace environment. An improved analytic hierarchy process and an entropy weight method are used to obtain the subjective and objective weights of the evaluation indicators, respectively. Then, a gaming model is used to determine the optimal linear combination coefficients of these subjective and objective weights by minimizing the deviation, in order to find the combined weights. A synthetic evaluation model based on combined weighting and a cloud model is developed. Taking Dalu water aerodrome in the City of Zhenjiang as a case study, study results show that the evaluation result of the site of water aerodrome is good. There hasn't been no accident since the water aerodrome is under operation. The evaluation result is consistent with observed safety condition of the airport. The cloud model used can well address the uncertainty in the selection of the membership function by balancing the randomness and fuzziness of data in the evaluation process of any evaluation method using fuzzy mathematics, which further increases the reliability of the evaluation results from the proposed method. Study results show that the evaluation outcomes from the proposed method are similar to those from the classical fuzzy comprehensive evaluation method, which verifies the validity as well as the practicability of the model. The proposed model can be applied for the site selection of the water aerodromes to be built, and also for the evaluation of the safety of existing water aerodromes.
- traffic safety /
- water aerodrome /
- site evaluation /
- combination weighting /
- game theory /
- cloud model

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图 1 水上机场场址评价指标体系

Figure 1. An evaluation system of water aerodrome site

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图 2 云图示意图

Figure 2. Cloud model map diagram

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图 3 标准评价云

Figure 3. Standard evaluation cloud model

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图 4 水上机场概位

Figure 4. General location of seadrome

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图 5 指标权重对比

Figure 5. Index weight comparison

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图 6 A₁，A₂，A₃指标云图

Figure 6. Index cloud map of A₁, A₂ and A₃

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图 7 B₁，B₂，B₃，B₄指标云图

Figure 7. Index cloud map of B₁, B₂, B₃ and B₄

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图 8 C₁，C₂指标云图

Figure 8. Index cloud map of C₁ and C₂

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图 9 D₁，D₂指标云图

Figure 9. Index cloud map of D₁ and D₂

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图 10 综合评价云与标准评价云对比

Figure 10. Comparison between comprehensive evaluation cloud and standard evaluation cloud

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表 1 水上机场场址评价指标

Table 1. Evaluation indicators of water aerodrome site

	指标	描述
	风速A₁	机体受风的影响较大，风速越大，起降危险程度越高
气象环境A	跑道与盛行风夹角A₂	跑道方向应尽可能与盛行风方向一致，二者夹角越大，飞机起降越危险
	能见度不良天数A₃	水上飞机目视起降，能见度不良天气对飞机起降安全影响大
	流速B₁	水上跑道水流流速宜小于1.5 m/s
水文环境B	水深B₂	水深应满足机型满载吃水要求，且有足够的富裕水深
水文环境B	潮汐B₃	潮差越大，可能致低潮时水深不足而需疏浚跑道，建设成本就越大
	浪高B₄	水面扰动以适度为宜，涟漪或者浪高在7.5~15 cm之间为好
通航环境C	船舶流量C₁	水上机场附近水域船舶流量越大，对水上飞机运营安全影响越大
通航环境C	与航道间距C₂	机场与航道的间距越近，船舶与飞机的相互影响越大，碰撞危险程度越高
起降水域的空域环境D	与桥梁、架空电缆距离D₁	桥梁、架空电缆与跑道前方距离越近，对水上飞机的起降安全影响越大
起降水域的空域环境D	航道与跑道前方交叉，船舶水线上高度D₂	船舶水线以上高度越高，飞机起降跨越船舶上空时危险程度越大

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表 2 评价等级云数字特征

Table 2. Cloud digital characteristics of evaluation grade

评价等级	区间	E_x	E_n	H_e
差	[0, 2]	1	0.33	0.05
较差	[2, 4]	3	0.33	0.05
一般	[4, 6]	5	0.33	0.05
较好	[6, 8]	7	0.33	0.05
好	[8, 10]	9	0.33	0.05

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表 3 指标权重

Table 3. Index weights

指标	A₁	A₂	A₃	B₁	B₂	B₃	B₄	C₁	C₂	D₁	D₂
w₁	0.117 3	0.117 3	0.064 0	0.117 3	0.051 6	0.057 4	0.081 8	0.146 5	0.081 8	0.092 5	0.072 5
w₂	0.089 3	0.118 6	0.1028	0.051 9	0.075 5	0.108 4	0.061 2	0.1529	0.103 8	0.076 5	0.059 1
w^*	0.099 8	0.118 1	0.088 2	0.076 5	0.066 5	0.089 3	0.069 0	0.150 5	0.095 5	0.082 5	0.064 1

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表 4 指标等级划分

Table 4. Index grade division

等级指标	好[8, 10]	较好[6, 8]	一般[4, 6]	较差[2, 4]	差[0, 2]
风速（m/s)	< 3	3~4	4~5	5~6	> 6
与盛行风夹角（°)	< 18	18 ~36	36~54	54~72	72~90
雾天/d	< 20	20 ~40	40~60	60~80	> 80
流速（m/s)	< 0.75	0.75 ~1.5	1.5 ~2.25	2.25~3	> 3
水深（m)	> 2.5	2.0 ~2.5	1.5 ~2.0	1.0 ~1.5	< 1.0
潮差（m)	< 0.6	0.6 ~1.2	1.2 ~1.8	1.8 ~2.4	> 2.4
浪高/(cm)	< 15	15~30	30~45	45~60	> 60
船舶流量(艘/d)	< 200	200~300	300~400	400~500	> 500
与航道间距/(m)	> 200	150~200	100~150	50~100	< 50
桥梁架空电缆与跑道前方距离(m)	> 2 500	> 2 000~2 500	> 1 500~2 000	> 1 000~1 500	< 1 000
船舶水线上高度/(m)	≤ 5	> 5 ~10	> 10 ~15	> 15 ~20	> 20

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表 5 量纲统一数据

Table 5. Dimensional unified data

指标						评分值
A₁	8.3	8.6	7.9	8.4	8.2	7.8	8.3	8.6	8.9	8.7	8.1	8.5
A₂	5.9	6.0	5.5	5.6	6.1	6.2	5.7	5.5	5.8	6.1	6.2	6.1
A₃	5.2	5.1	5.4	5.5	5.6	5.3	5.4	5.0	5.1	4.9	5.4	5.5
B₁	8.7	8.8	8.5	8..6	8.9	8.8	8.3	8.4	8.6	8.1	8.2	8.7
B₂	5.2	5.1	5.3	5.4	5.2	5.0	5.3	4.9	5.1	5.1	4.8	4.9
B₃	6.6	6.8	6.6	6.7	7.4	7.5	7.2	6.8	6.9	7.0	7.2	7.1
B₄	7.9	8.1	7.8	7.6	8.1	8.2	7.9	7.5	7.8	8.2	7.7	7.5
C₁	9.1	8.8	7.8	8.7	8.6	7.9	8.6	8.7	7.7	8.4	8.5	8.3
C₂	8.5	8.6	8.4	7.7	8.7	8.6	8.2	8.3	7.8	7.9	8.4	8.7
D₁	8.3	8.4	8.1	7.8	8.2	8.5	8.4	7.9	7.7	8.6	8.5	8.4
D₂	8.4	8.8	8.1	8.3	8.2	8.4	9.0	8.5	8.6	8.7	8.8	8.7

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表 6 指标云数字特征参数

Table 6. Indicator cloud digital characteristic parameters

指标	E_x	E_n	H_e
A₁	8.358 3	0.353 2	0.135 9
A₂	5.891 7	0.309 5	0.160 9
A₃	5.283 3	0.254 5	0.126 3
B₁	8.550 0	0.284 8	0.128 8
B₂	5.108 3	0.195 6	0.068 5
B₃	6.983 3	0.341 8	0.162 0
B₄	7.858 3	0.284 8	0.129 1
C₁	8.425 0	0.461 4	0.170 5
C₂	8.316 7	0.383 6	0.160 1
D₁	8.233 3	0.334 2	0.1543
D₂	8.541 7	0.307 6	0.145 2

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