基于地理信息系统分析的吉打巴陵山洪潜在指数(FFPI)及其物理环境因素

Q4 Social Sciences
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引用次数: 0

摘要

2022年7月4日,吉打巴陵Sungai Kupang发生毁灭性洪水,造成3人死亡,17栋房屋被毁或受损,3546名居民受到影响,损失估计为2591万令吉。洪水是由高地地区的强降雨引发的,导致多处山体滑坡同时发生。山体滑坡导致四条主要河流支流出现泥石流现象,最终导致低地和下游村庄发生泥石流和泥石流。本研究的目的是分析巴陵山洪暴发的位置,并根据已确定的土地物理因素估计山洪暴发的可能性。本研究还确定了巴陵盆地发生山洪的高潜力关键区域,并与少数流域发生的历史洪水事件和遥感图像相比,评估了FFPI模型的有效性和适用性。本研究采用2003年首次建立的FFPI模型,通过考虑坡度、土地覆盖、土壤数据和植被,对巴陵发生的山洪进行了分析。FFPI技术应用于五种情况,以确定潜在的山洪暴发,所使用的值也基于参考文献。指数上的值1表示发生山洪暴发的可能性最小,而值10表示发生山洪的可能性最高。根据研究结果,研究区域发生指数值为7的山洪暴发的可能性很高。当数值超过50%时,在所有情况下,严重山洪的危险等级都存在于整个研究区域。然后,利用山洪及其热点地区的历史信息进行比较,并利用卫星图像确定洪水的真实规模。这对于在同一地点减少洪水的影响以及管理风险和规划减灾行动也很重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Employing the Flash Flood Potential Index (FFPI) with Physical Environmental Factors in Baling, Kedah through GIS Analysis
On July 4, 2022, Sungai Kupang, Baling, Kedah experienced a devastating flood that caused 3 fatalities, destroyed or damaged 17 houses, affected 3,546 residents, and resulted in losses estimated at RM25.91 million. The flood was triggered by heavy rainfall in the highland area, which caused multiple landslides to occur simultaneously. The landslides led to a debris flow phenomenon in four main river branches, ultimately resulting in a tragic debris and mud flood in the lowlands and downstream villages. The aim of this study is to analyze the location of flash flood occurrences in Baling and to estimate the likelihood of flash floods based on the identified land physical factors. This study also identifies the critical area of Baling basin that have high potential for a flash flood and evaluates the effectiveness and applicability of the FFPI model compared with historical flood events and remote sensing imagery which have occurred in the few watersheds area. The FFPI model, which was created for the first time in 2003, is used in this study to analyze the flash flood that occurred in Baling by considering slope, land cover, soil data, and vegetation. The FFPI technique is applied in five scenarios to determine the flash flood potential, and the value used is also based on the references. A value of 1 on the index denotes a minimal probability of flash floods, while a value of 10 indicates the highest probability. Based on the findings, the study area had a high possibility of having a flash flood at an index value of 7. The danger level of a severe flash flood is present throughout the research region in all scenarios when the value is more than 50%. The outcome is then utilized to do comparisons using historical information on flash floods and their hotspots area, as well as utilizing satellite imagery to determine the true scale of the flood. This is also important to reduce the impact of floods occurrence in the same place as well managing risk and to plan for disaster-mitigation operations.
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来源期刊
International Journal of Geoinformatics
International Journal of Geoinformatics Social Sciences-Geography, Planning and Development
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