Avalanche is one of a variety of mass movements that refers to the rapid movement of masses of snow in the direction of slope gradient. Avalanche drives in addition to snow, rock and soil and plant and damage the communication lines, buildings and power lines on the way to the avalanche. In this research, domains with avalanche potential were zoned using artificial neural network model (MLP). In order to study the model of the data of 39 fields that occurred in avalanche, the network was introduced and the factors affecting the avalanche include slope, gradient, altitude, land use, geology, weather data including precipitation and temperature, and the network of waterways and communication lines It was introduced as input after normalizing to the network, and finally, after the test and error, to achieve the minimum error and maximum structural accuracy with an input layer architecture with 10 neurons, a hidden layer with 15 neurons and an output layer for training And network learning. Out of 39 data that was previously in the avalanche that was introduced as black spots to the network, 70% was used for training and 30% for the test, and the model was carefully trained with 90.4%. Finally, after zoning of avalanche slopes with 88.6 percent accuracy in zoning, the factor for slope, slope and elevation was the most important factor in avalanche occurrence in the region, respectively.