基于RF-RUSLE模型的水土流失性公路自然灾害风险评估——以重庆市巴南区为例

doi:10.3969/j.issn.1674-0696.2020.11.17

重庆交通大学学报（自然科学版） ›› 2020, Vol. 39 ›› Issue (11): 114-121.DOI: 10.3969/j.issn.1674-0696.2020.11.17

• 港口航道·水利水电·资源环境 • 上一篇下一篇

基于RF-RUSLE模型的水土流失性公路自然灾害风险评估——以重庆市巴南区为例

牟凤云1，杨猛1，余情2，刘振涛3

(1. 重庆交通大学建筑与城市规划学院，重庆 400074； 2. 重庆交通大学土木工程学院，重庆 400074； 3. 贵州省科技信息中心，贵州贵阳 550001)

收稿日期:2020-04-29 修回日期:2020-10-16 出版日期:2020-11-19 发布日期:2020-11-23
作者简介:牟凤云(1979—)，女，山东高密人，教授，博士，主要从事公路洪灾机制、国土资源遥感、3S 技术集成与应用等方面的研究。E-mail:mfysd@cqjtu.edu.cn 通信作者：杨猛(1993—)，男，重庆人，硕士研究生，研究方向为公路灾害防治、3S 技术集成与应用。E-mail1:836819545@qq.com
基金资助:
重庆市自然科学基金面上项目（cstc2019jcyj-msxmX0626）；国家重点研发计划项目（2019YFB2102503）；国家自然科学基金项目（41601564）；黔科中引地（﹝2016﹞4009）

Risk Assessment of Natural Disasters on Highway Caused by Soil Erosion Based on RF-RUSLE Model: A Case Study of Banan District in Chongqing

MU Fengyun1，YANG Meng1，YU Qing2，LIU Zhentao3

(1. School of Architecture and Urban Planning, Chongqing Jiaotong University, Chongqing 400074, China; 2. School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China; 3. Guizhou Science and Technology Information Center, Guiyang 550001, Guizhou, China)

Received:2020-04-29 Revised:2020-10-16 Online:2020-11-19 Published:2020-11-23

摘要/Abstract

摘要： 选择降雨量、土壤类型、植被覆盖度、坡度、坡位和整治力度等6个指标建立风险指标体系，构建RF-RUSLE模型，分析重庆市巴南区水土流失程度及公路灾害与水土流失之间的关系；结合区域路网格局，对重庆市巴南区水土流失性公路自然灾害风险进行了评估与预测，对研究区水土流失性公路自然灾害风险等级进行了划分，探讨了研究区公路自然灾害致灾因素。结果表明：重庆市巴南区水土保持较好，但降雨量侵蚀严重，水土流失程度介于轻度～中度；巴南区水土流失性公路自然灾害风险等级总体为中度风险，南部、西南部为公路灾害高风险区域；在各乡镇交界处，公路灾害防治薄弱，水土流失性公路自然灾害发生概率极高，降雨量和坡度为主要致灾因素，需要重点监测与防治。

关键词: 道路工程, 环境工程, 公路自然灾害, 水土流失, RF-RUSLE模型, 风险评估

Abstract: Six indicators including rainfall, soil type, vegetation coverage, slope, slope position and remediation intensity were selected to establish a risk indicator system, and a RF-RUSLE model was constructed to analyze the degree of soil erosion and the relationship between highway disasters and soil erosion in Banan district of Chongqing. Combined with the regional road network, the natural disaster risk of soil erosion highway in Banan district of Chongqing was evaluated and predicted, and the natural disaster risk level of soil erosion highway in the study area was classified. The factors causing natural disasters on highways in the study area were discussed. The results show that the soil and water conservation in Banan district of Chongqing is relatively better, but the rainfall erosion is serious, and the degree of soil erosion ranges from mild to moderate; the natural disaster risk grade of soil erosion highway in Banan district of Chongqing is generally medium risk, and the southern part and the southwestern part are high risk areas of highway disaster; at the junction of villages and towns, the prevention and control of highway disasters is weak, and the probability of natural disasters caused by soil erosion is extremely high. Rainfall and slope are the main disaster-causing factors, which need to be monitored and controlled.

Key words: highway engineering, environmental engineering, natural disaster of highway, soil erosion, RF-RUSLE model, risk assessment

中图分类号:

U417.1
X43

牟凤云1，杨猛1，余情2，刘振涛3. 基于RF-RUSLE模型的水土流失性公路自然灾害风险评估——以重庆市巴南区为例[J]. 重庆交通大学学报（自然科学版）, 2020, 39(11): 114-121.

MU Fengyun1，YANG Meng1，YU Qing2，LIU Zhentao3. Risk Assessment of Natural Disasters on Highway Caused by Soil Erosion Based on RF-RUSLE Model: A Case Study of Banan District in Chongqing[J]. Journal of Chongqing Jiaotong University(Natural Science), 2020, 39(11): 114-121.

参考文献

［1］ CONTRERAS-ZARAZA G, VILLICAA-GARCA E, CANSINO- LOEZA B, et al. Environmental impact and occupational hazard evaluation on intensified processes to produce
diphenyl carbonate［J］. Computers & Chemical Engineering, 2018, 122:19-30.
［2］ BALLESTEROS-CNOVAS J A, CZAJKA B, JANECKA K, et al. Flash floods in the Tatra Mountain streams: Frequency and triggers［J］.The Science of the Total
Environment, 2015, 511: 639-648.
［3］ LLASAT M C, MARCOS R, LLASAT-BOTIJA M, et al. Flash flood evolution in north-western Mediterranean［J］.Atmospheric Research, 2014, 149: 230-243.
［4］ GARCA-PINTADO J, BARBER G G, ERENA M, et al. Calibration of structure in a distributed forecasting model for a semiarid flash flood: Dynamic surface
storage and channel roughness ［J］. Journal of Hydrology (Amsterdam), 2009,377(1):165-184.
［5］杨乐，陈杰.基于灰色关联法的地质灾害危险性影响因子分析［J］.重庆交通大学学报(自然科学版)，2017，36(6)：80-84，91.
YANG Le, CHEN Jie. Impact factors analysis of geological hazard risk based on Grey correlation method ［J］. Journal of Chongqing Jiaotong University (Natural
Science), 2017,36 (6): 80-84, 91.
［6］李家春,尹超,田伟平,等.中国公路自然灾害易损性评价［J］.北京工业大学学报,2015,41(7):1067-1072.
LI Jiachun, YIN Chao, TIAN Weiping, et al. Vulnerability assessment of natural disasters of chinese highway ［J］. Journal of Beijing Polytechnic University,
2015,41 (7): 1067-1072.
［7］熊俊楠,李进,程维明,等.西南地区山洪灾害时空分布特征及其影响因素［J］.地理学报,2019,74(7):1374-1391.
XIONG Junnan, LI Jin, CHENG Weiming, et al. Spatial-temporal distribution and the influencing factors of mountain flood disaster in southwest China ［J］. Acta
Geographica Sinica, 2019,74 (7): 1374-1391.
［8］周仲礼,张乾荣,曹赛男.基于决策树理论的泥石流危险性评价研究［J］.重庆交通大学学报(自然科学版),2017,36(4):76-80.
ZHOU Zhongli, ZHANG Qianrong, CAO Sainan. Risk assessment of debris flow based on decision tree theory ［J］. Journal of Chongqing Jiaotong University (Natural
Science), 2017,36 (4): 76-80.
［9］黄明奎,马璐.重庆市地质灾害气候性诱发机理分析［J］.重庆交通大学学报(自然科学版),2017,36(11):66-70.
HUANG Mingkui, MA Lu. Climatically induced mechanism of geological disasters in Chongqing ［J］. Journal of Chongqing Jiaotong University (Natural Science),
2017,36 (11): 66-70.
［10］牟凤云，杨猛，林孝松，等.基于机器学习算法模型的巫山县洪水灾害研究［J］.中山大学学报(自然科学版)，2020，59(1)：105-113.
MU Fengyun, YANG Meng, LIN Xiaosong, et al. The flood disasters in Wushan county based on machine learning algorithm model［J］.Acta Scientiarum Naturalium
Universitatis Sunyatseni, 2020, 59(1): 105-113.
［11］史培军,孔锋,叶谦,等.灾害风险科学发展与科技减灾［J］.地球科学进展,2014,29(11):1205-1211.
SHI Peijun, KONG Feng, YE Qian, et al. Disaster risk science development and disaster risk reduction using science and technology ［J］. Advances in Earth Science,
2014,29 (11): 1205-1211.
［12］杨猛,牟凤云,林孝松,等.多情景降雨-径流的巫山县公路洪灾预警研究［J］.浙江大学学报(理学版),2019,46(6):755-761.
YANG Meng, MU Fengyun, LIN Xiaosong, et al. Flood warning of Wushan county highway based on rainfall and runoff under multi-condition ［J］. Journal of Zhejiang
University (Science Edition), 2019, 46(6): 755-761.
［13］郭良,张晓蕾,刘荣华,等.全国山洪灾害调查评价成果及规律初探［J］.地球信息科学学报,2017,19(12):1548-1556.
GUO Liang, ZHANG Xiaolei, LIU Ronghua, et al. Achievements and preliminary analysis on China national flash flood disasters investigation and evaluation ［J］.
Journal of Geo-Information Science, 2017,19 (12): 1548-1556.
［14］ ELFATIH A R, DAVID M, TOBIAS L, et al. The utility of AISA eagle hyperspectral data and random forest classifier for flower mapping［J］. Remote Sensing
(Basel, Switzerland), 2015, 7(10):13298-13318.
［15］周超,方秀琴,吴小君,等.基于三种机器学习算法的泥石流灾害风险评估［J］.地球信息科学学报,2019,21(11):1679-1688.
ZHOU Chao, FANG Xiuqin, WU Xiaojun, et al. Risk assessment of mountain torrents based on three machine learning algorithms ［J］. Journal of Geo-Information
Science, 2019, 21(11): 1679-1688.
［16］ KANG K, RYU H. Predicting types of occupational accidents at construction sites in Korea using random forest model［J］. Safety Science, 2019,120: 226-236.
［17］ SHEARMAN T M, VARNER J M, HOOD S M, et al. Modelling post-fire tree mortality: Can random forest improve discrimination of imbalanced data?［J］. Ecological
Modelling, 2019, 414: 108855. DOI: 10.1016/j.ecolmodel.2019.108855.
［18］李家春,黄丽珍,田伟平,等.公路自然灾害类型划分［J］.长安大学学报(自然科学版),2011,31(2):33-37.
LI Jiachun, HUANG Lizhen, TIAN Weiping, et al. Type classification on natural disasters of highway ［J］. Journal of Changan University (Natural Science
Edition), 2011,31 (2): 33-37
［19］牟凤云,龙秋月,余情,等.基于SCS模型的巫山县降雨量径流多情景模拟［J］.重庆交通大学学报(自然科学版),2020,39(2):118-125.
MU Fengyun, LONG Qiuyue, YU Qing, et al. Multi-scenario simulation of rainfall and runoff in Wushan county based on SCS model ［J］. Journal of Chongqing Jiaotong
University (Natural Science), 2020,39 (2): 118-125.

[1]	张淑宝. 一种中央分隔带分离式改进混凝土护栏的研究[J]. 重庆交通大学学报（自然科学版）, 2021, 40(07): 107-111.
[2]	赵伟. 纳米蒙脱石/SBS复掺改性沥青性能试验研究[J]. 重庆交通大学学报（自然科学版）, 2021, 40(07): 118-122.
[3]	郭鹏1，陈思贤1，曹志国1，刘俊1，孟建玮2，鲜江林3. 复配废机油再生剂的制备及性能研究[J]. 重庆交通大学学报（自然科学版）, 2021, 40(06): 87-91.
[4]	肖庆一1,2，赵鹏1，孙博伟3，张怡4，丁啸5. 废植物油再生沥青结合料性能研究[J]. 重庆交通大学学报（自然科学版）, 2021, 40(06): 92-98.
[5]	何丽红1,2,温仙仙1,2,侯艺桐1,2,邓稳1,2. 阴离子乳化沥青粒径大小及分布影响因素分析[J]. 重庆交通大学学报（自然科学版）, 2021, 40(06): 99-104.
[6]	姚爱玲1，王军伟2，许敏3，杨孟乾4，杨涛1. 水泥稳定碎石基层沥青路面隆起开裂数值分析[J]. 重庆交通大学学报（自然科学版）, 2021, 40(06): 105-111.
[7]	赵全满，任瑞波，刘瑶，李志刚，户桂灵. 城市道路检查井井周路面破坏机理[J]. 重庆交通大学学报（自然科学版）, 2021, 40(05): 87-94.
[8]	王志祥1,2，李建阁1，陈楚鹏2. 基于变权重评价的沥青路面使用性能灰色预测[J]. 重庆交通大学学报（自然科学版）, 2021, 40(05): 95-101.
[9]	肖庆一1，2，3，封仕杰1，孙立东1，陈向伟1. 碱渣掺量对不同龄期下半刚性再生基层力学性能研究[J]. 重庆交通大学学报（自然科学版）, 2021, 40(05): 102-109.
[10]	张占梅1，石瑞琦1，任筱雨1，潘云霞2，李锐3. “A2/O+混凝沉淀+纤维转盘过滤”工艺在污水处理厂升级改造中的工程实践[J]. 重庆交通大学学报（自然科学版）, 2021, 40(05): 116-121.
[11]	潘兵宏1，2，任卉2，单慧敏1，霍永富1. 客货分离整体式路基互通出口同分带开口长度研究[J]. 重庆交通大学学报（自然科学版）, 2021, 40(04): 83-90.
[12]	孔令云1，邹胜楠1，黄麟钬2，吴海鹰3，余苗1，杨博1. 复合式路面疲劳裂纹扩展数值模拟研究[J]. 重庆交通大学学报（自然科学版）, 2021, 40(04): 91-97.
[13]	凌天清1，魏巧2，李传强3，袁海2，杨清尘3. 生物柴油-塑料裂解蜡复合温拌改性沥青性能研究[J]. 重庆交通大学学报（自然科学版）, 2021, 40(04): 98-104.
[14]	王清洲1，孙超1，檀奥龙2，魏连雨1. 膨胀性泥岩重塑土膨胀力试验研究[J]. 重庆交通大学学报（自然科学版）, 2021, 40(04): 112-117.
[15]	艾海男1，周漫宇1，周涛2，詹昊1，吕逸韬1. P-SMFC体系中磷形态转化及去除效能研究[J]. 重庆交通大学学报（自然科学版）, 2021, 40(04): 118-126.

基于RF-RUSLE模型的水土流失性公路自然灾害风险评估——以重庆市巴南区为例

Risk Assessment of Natural Disasters on Highway Caused by Soil Erosion Based on RF-RUSLE Model: A Case Study of Banan District in Chongqing

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics