利用机器学习和 AHP-MCE 技术开发洪水淹没监测、洪水灾害和土壤侵蚀易发性自动评估方法

IF 3.8 Q2 ENVIRONMENTAL SCIENCES
A. Jaya Prakash, Sazeda Begam, Vít Vilímek, Sujoy Mudi, Pulakesh Das
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引用次数: 0

摘要

随着哨兵 1 号微波数据的出现,利用公开卫星数据进行大规模洪水监测成为可能,该数据可实现日夜近实时(间隔 6 天)洪水测绘,即使在多云的季风季节也是如此。近实时洪水淹没区自动识别涉及先进的地理空间数据处理平台,如谷歌地球引擎和强大的方法论(大津算法)。当前的研究利用机器学习(ML)算法,采用哨兵-1 微波数据绘制印度阿萨姆邦的洪水范围图。我们结合有关天气条件、土壤和地形特征的多源数据,生成了洪水灾害和土壤侵蚀易感性地图。随机森林(RF)、分类与回归工具(CART)和支持向量机(SVM)ML 算法被用于生成洪水灾害地图。此外,我们还采用了多标准评价(MCE)分析层次过程(AHP)来绘制土壤侵蚀易发性地图。RF 模型的预测准确率最高(总体准确率 [OA] > 82%),其次是 SVM(OA > 82%)和 CART(OA > 81%)。超过 26% 的研究区域显示为洪水灾害高发区,约 60% 的区域显示洪水导致水土流失的可能性高且严重。自动洪水测绘平台是应急响应人员和决策者的重要资源,因为它有助于通过确定合适的区域和适当的后勤路线规划来指导救灾活动,并提高应急响应工作的准确性和及时性。定期绘制的洪水淹没图将有助于长期规划和决策、洪水管理、土壤和生物多样性保护、土地退化、可持续农业干预规划、作物保险和气候适应性研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of an automated method for flood inundation monitoring, flood hazard, and soil erosion susceptibility assessment using machine learning and AHP–MCE techniques
Operational large-scale flood monitoring using publicly available satellite data is possible with the advent of Sentinel-1 microwave data, which enables near-real-time (at 6-day intervals) flood mapping day and night, even in cloudy monsoon seasons. Automated flood inundation area identification in near-real-time involves advanced geospatial data processing platforms, such as Google Earth Engine and robust methodology (Otsu’s algorithm). The current study employs Sentinel-1 microwave data for flood extent mapping using machine learning (ML) algorithms in Assam State, India. We generated a flood hazard and soil erosion susceptibility map by combining multi-source data on weather conditions and soil and terrain characteristics. Random Forest (RF), Classification and Regression Tool (CART), and Support Vector Machine (SVM) ML algorithms were applied to generate the flood hazard map. Furthermore, we employed the multicriteria evaluation (MCE) analytical hierarchical process (AHP) for soil erosion susceptibility mapping. The highest prediction accuracy was observed for the RF model (overall accuracy [OA] > 82%), followed by the SVM (OA > 82%) and CART (OA > 81%). Over 26% of the study area indicated high flood hazard-prone areas, and approximately 60% showed high and severe potential for soil erosion due to flooding. The automated flood mapping platform is an essential resource for emergency responders and decision-makers, as it helps to guide relief activities by identifying suitable regions and appropriate logistic route planning and improving the accuracy and timeliness of emergency response efforts. Periodic flood inundation maps will help in long-term planning and policymaking, flood management, soil and biodiversity conservation, land degradation, planning sustainable agriculture interventions, crop insurance, and climate resilience studies.
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来源期刊
Geoenvironmental Disasters
Geoenvironmental Disasters Social Sciences-Geography, Planning and Development
CiteScore
8.90
自引率
6.20%
发文量
22
期刊介绍: Geoenvironmental Disasters is an international journal with a focus on multi-disciplinary applied and fundamental research and the effects and impacts on infrastructure, society and the environment of geoenvironmental disasters triggered by various types of geo-hazards (e.g. earthquakes, volcanic activity, landslides, tsunamis, intensive erosion and hydro-meteorological events). The integrated study of Geoenvironmental Disasters is an emerging and composite field of research interfacing with areas traditionally within civil engineering, earth sciences, atmospheric sciences and the life sciences. It centers on the interactions within and between the Earth''s ground, air and water environments, all of which are affected by climate, geological, morphological and anthropological processes; and biological and ecological cycles. Disasters are dynamic forces which can change the Earth pervasively, rapidly, or abruptly, and which can generate lasting effects on the natural and built environments. The journal publishes research papers, case studies and quick reports of recent geoenvironmental disasters, review papers and technical reports of various geoenvironmental disaster-related case studies. The focus on case studies and quick reports of recent geoenvironmental disasters helps to advance the practical understanding of geoenvironmental disasters and to inform future research priorities; they are a major component of the journal. The journal aims for the rapid publication of research papers at a high scientific level. The journal welcomes proposals for special issues reflecting the trends in geoenvironmental disaster reduction and monothematic issues. Researchers and practitioners are encouraged to submit original, unpublished contributions.
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