![]() Comb Chem High Throughput Screen 20(2):164–173 Zhang Q, Sun X, Feng K et al (2017) Predicting citrullination sites in protein sequences using mRMR method and random forest algorithm. Lin P, Yang L (2019) Urban classification based on random forest algorithm. Rewade AD, Mohod SW, Bargat SP (2019) Content based alternate medicine recommendation by using random forest algorithm. Levantesi S, Nigri A (2020) A random forest algorithm to improve the Lee–Carter mortality forecasting: impact on q-forward. Zhang X, Huang W, Lin X et al (2020) Complex image recognition algorithm based on immune random forest model. Wu Q, Wang H, Yan X et al (2019) MapReduce-based adaptive random forest algorithm for multi-label classification. Rewade AD, Mohod SW (2018) Content based alternate medicine recommendation by using random forest algorithm a review. Guo J, Wang J, Li Q et al (2019) Construction of prediction model of neural network railway bulk cargo floating price based on random forest regression algorithm. Mohammady M, Pourghasemi HR, Amiri M (2019) Land subsidence susceptibility assessment using random forest machine learning algorithm. Wang Y, Xia H, Yuan X et al (2018) Distributed defect recognition on steel surfaces using an improved random forest algorithm with optimal multi-feature-set fusion. He S, Chen W, Liu H et al (2019) Gene pathogenicity prediction of Mendelian diseases via the random forest algorithm. Joshuva A, Sugumaran V (2017) Fault diagnosis for wind turbine blade through vibration signals using statistical features and random forest algorithm. Xu Y, Zhang J, Gong X et al (2016) A method of real-time traffic classification in secure access of the power enterprise based on improved random forest algorithm. Polan D, Brady S, Kaufman R (2016) Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study. The research results show that the combination of random forest algorithm and GIS technology is convenient for analyzing the spatial pattern and internal laws of flood risk, and has good applicability. Generally speaking, the model prediction accuracy is high. At the same time, there are a total of 85 samples that have experienced flood disasters, of which only six have been misjudged as no flood disasters. Using the natural break point method of ArcGIS 10.1 platform, the study area is divided according to the magnitude of the flood disaster risk value. In the experimental part, this research uses layer overlay to determine the number and types of affected areas. Finally, use ArcGIS spatial analysis tool map algebra function to model, carry out flood risk assessment in different periods, and use spatial analysis function to extract the median value to point function to extract the flood inundation depth of the study area in a specific scenario. Second, the random forest algorithm is used as the weight of each parameter of the flood disaster index model. First, use ArcGIS10.1 to analyze and integrate each hazard factor into the flood disaster report index model. This method is based on the characteristics of natural disaster-causing factors in the study area, selects an appropriate grid size, and finally realizes the function of visual expression of regional disaster risk. This study uses the special functions of GIS to collect, manage, and analyze data to propose a method of flood disaster risk assessment based on GIS. This research mainly discusses flood disaster risk assessment based on random forest algorithm. Where the pred column is what the predict function currently returns, and the first 3 columns show what proportion of the 500 trees gave which prediction.With the frequent occurrence of natural disasters, timely warning of flood disasters has become an issue of concern. I then use it to make predictions using the predict() function. ![]() I built a random forest model called iris_class *. ![]()
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