Abstract:
This paper is the result of geological hazard survey engineering.
Objective The Yunnan section of the Nujiang River Basin is located in the western Hengduan Mountains. It is dominated by deep mountain canyon landforms and has always been a high-incidence area of debris flows, so it is of great significance to carry out an accurate evaluation of mudslide susceptibility in the region for hazard prevention and mitigation.
Methods Based on the data obtained from geological survey projects, this study takes the basin as the evaluation unit, and uses statistical models such as information volume model, frequency ratio model and certainty coefficient model to couple with machine learning models such as random forest model and BP neural network model to evaluate the susceptibility of debris flow hazards in the mainstream Nujiang River Basin. The evaluation accuracy and reliability of various coupling models are compared and analyzed through model evaluation parameters, seed area unit index and ROC curve before we have identified the optimal coupling integrated model(s).
Results The ranking of various integrated models from best to worst in terms of evaluation effect is as follows: Frequency ratio-Random forest > Information volume-Random forest > Certainty coefficient-Random forest > Frequency ratio-Neural network > Information volume-Neural network>Certainty coefficient-Neural network.
Conclusions The frequency ratio-random forest model has the best prediction accuracy and evaluation efficacy among all integrated models; Under the same statistical model conditions, the random forest model demonstrates a significant advantage over the neural network model; And furthermore, in terms of the adaptability of data after statistical model processing and machine learning model, the frequency ratio model performs better than the information volume model, which in turn performs better than the certainty coefficient model.
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