Taking the western Qinling Mountain, in the southern Shaanxi Province of china, as an example, based upon comprehensive analysis of geological data for 20 debris flow gullies, the author has put forward a series of indices system and has developed the immune genetic neural network system, which can quantitatively evaluate the dangerous degree of mine debris flow. This software system manage initial data through Access’s data-base technology, and determine and optimize the hidden layer network by immune genetic algorithm, as well as achieve the dangerous degree evaluation of mine debris flow by virtue of artificial neural network which has been successfully trained. The calculating results of mine debris flow examples testify that this method is reliable and can accurately evaluate the dangerous degree of mine debris flow. These evaluation results have some important instructive significance for the disaster prevention and reduction of mine debris flow.

Taking the western Qinling Mountain, in the southern Shaanxi Province of china, as an example, based upon comprehensive analysis of geological data for 20 debris flow gullies, the author has put forward a series of indices system and has developed one evaluation system called “dangerous degree evaluation system of mine debris flow based on IGA-BP”. This system adopts Visual Basic 6.0 and Access technology to manage database, adopts immune genetic algorithm to optimize the hidden layer structure and network parameters of BP neural network and adopts sample model of mine debris flow whose dangerous degree has been known to realize the BP neural network evaluation of the debris flow risk which to be determined. The calculating results show that this evaluation method has high reliability and simplicity of operation, and it can make comprehensive evaluation precisely. The evaluation results have important guiding significance in the prevention and reduction mine debris flow.