Detection of satellite data-based flood-prone areas using logistic regression in the central part of Java Island
Detection of satellite data-based flood-prone areas using logistic regression in the central part of Java Island
复制标题
利用逻辑回归检测爪哇岛中部基于卫星数据的洪水易发区
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
P. Santoso
中科院分区：
文献类型：
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作者：
G. Pratidina;Suroso;P. Santoso
BNPB（2019）中记录的自然灾害历史解释说，爪哇中部（中爪哇省和日惹省特区）的自然灾害事件总数在全国发生频率中排名最高。在中爪哇发生的自然灾害中，洪水的数量仅次于滑坡和龙卷风灾害，约为1500次。导致水浸的各种因素无法消除。然而，更重要的是，如何控制洪水造成的影响，使它们能够得到适当的管理和监测。克服洪水威胁问题的一项努力是为洪水易发地区开发一种探测模型。在本研究中，采用logistic回归方法对洪水易发区域进行检测，该方法考虑了引起洪水的变量，如高程、土地坡度、河流距离、流量累积、降雨量和径流系数。建模结果得到了前面提到的变量/参数的系数，即截距（5.05766 - 16.13210）、降雨量（-0.01547 - 0.04075）、海拔（-0.02173 - -0.00592）、坡度（-0.28108 - -0.01940）、径流系数（-9.10476 - 7.15039）、河距（0.00038 - 0.00783）和流量积累（- 9.26342e -06 - 0.00309）。在329个洪水事件数据点中，该模型测试的成功程度为93.47826% -98.26087%，而不是洪水。
The history of natural disasters recorded in BNPB (2019) explains that the total number of natural disaster events in the central part of Java (Central Java Province and Special Region of Yogyakarta Province) ranks highest in terms of the number of frequency of occurrences nationally. Of the total natural disasters that have occurred in Central Java, the number of floods is ranked third after the landslide and tornado disaster, which is around 1500 disasters. Various factors that can cause flooding cannot be eliminated. However, what is more, necessary is how to control the impacts caused by floods so that they can be managed and monitored appropriately. One effort to overcome the problem of the threat of flooding is to develop a detection model for flood-prone areas. In this study, the detection of flood-prone areas was carried out by using a logistic regression method that takes into account the variables that cause flooding such as elevation, land slope, river distance, flow accumulation, rainfall, and runoff coefficients. The results of the modelling, obtained coefficients of the variables/parameters mentioned earlier, namely intercept (5.05766 – 16.13210), rainfall (-0.01547 – 0.04075), elevation (-0.02173 – -0.00592), slope (-0.28108 – -0.01940), runoff coefficient (-9.10476 – 7.15039), river distance (0.00038 – 0.00783), and flow accumulation (-9.26342E-06 – 0.00309). The level of success in this modelling testing was 93.47826% -98.26087% of 329 flood event data points and not floods.