Boulder falls in Hong Kong - insights from power law relationships and supervised machine learning

Intelligently predicting the frequency and volume of natural hazards, including boulder falls, attracts widespread attention in earth science and engineering communities. Taking Hong Kong, China, which is a highly developed and densely populated city, as a research example, the present paper statistically studies the occurrence of boulder falls based on power law relationships and supervised machine learning. The 2002-2016 boulder fall inventory of Hong Kong, which consists of 194 boulder falls, is compiled and analyzed in detail, including volume and spatial distributions. Based on the findings, a power law relationship with the b-value greater than one is particularly proposed for Hong Kong, which then allows major boulder falls (volume >= 1 m(3)) occurring in two subsequent years to be accurately predicted. In order to predict volume ranges of potential boulder falls, eight supervised machine learning algorithms are trained and validated based on sixty-five filtered samples with five represented features. Two key conclusions are drawn from the findings. First, the geological settings, altitudes, and gradients of source locations are demonstrated to be three triggering factors of different volume ranges of boulder falls. Second, the logistic regression algorithm has the best performance in predicting boulder fall volumes among all the algorithms. The f1-score of 5-fold cross-validation is 72.9%, and the prediction accuracy of the test dataset is 75.8%. We are confident that the methodologies and results in this paper will pave the way for analyzing other types of natural terrain hazards in addition to boulder falls in the long run.

Automated summary

Intelligently predicting the frequency and volume of natural hazards, including boulder falls, is of great interest in earth science and engineering communities. This study focuses on Hong Kong and proposes a power law relationship specifically for the region to accurately predict major boulder falls. The research identifies geological settings, altitudes, and gradients as triggering factors for different volume ranges of boulder falls, with the logistic regression algorithm showing the best performance in predicting boulder fall volumes.