Development of an Optimal Neural Network for Avalanche Forecast in Himalayan Region
نویسندگان
چکیده
This paper deals with the application of a well-known data mining technique, multi-layer back-propagation neural network, for forecasting of an avalanche in Himalayan region. Metrological and snow data for Himalayan region has been used for training the neural network. EasyNN-plus 6.0g, neural network software for Microsoft windows, is used for the development of an optimal neural networkPUSHPDEV. The system tries to model the decision making process of a pragmatic expert. PUSHPDEV can forecast whether an avalanche will trigger on a particular day from November to April. The network accepts eighteen inputs and produces an output whose value is zero or one, zero for no avalanche and one for avalanche on that day.
منابع مشابه
Avalanch Zoning Using Artificial Neural Network (MLP) Models (Case Study: Mountainous Area of North Alborz Province)
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). I...
متن کاملPrediction of monthly rainfall using artificial neural network mixture approach, Case Study: Torbat-e Heydariyeh
Rainfall is one of the most important elements of water cycle used in evaluating climate conditions of each region. Long-term forecast of rainfall for arid and semi-arid regions is very important for managing and planning of water resources. To forecast appropriately, accurate data regarding humidity, temperature, pressure, wind speed etc. is required.This article is analytical and its database...
متن کاملپیشبینی قیمت مسکن برای شهر اهواز: مقایسه مدل هدانیک با مدل شبکه عصبی مصنوعی
Determination and the estimation of the house price in urban areas has a great importance for governments, individual and state investors and common people. The mentioned estimation can be used in future planning and decision making of many urban and regional policies. In this regard, due to the vital importance of the house price in recent decades powerful and effective functions have been use...
متن کاملA Neural-Network Approach to the Modeling of the Impact of Market Volatility on Investment
In recent years, authors have focused on modeling and forecasting volatility in financial series it is crucial for the characterization of markets, portfolio optimization and asset valuation. One of the most used methods to forecast market volatility is the linear regression. Nonetheless, the errors in prediction using this approach are often quite high. Hence, continued research is conducted t...
متن کاملArtificial neural network forecast application for fine particulate matter concentration using meteorological data
Most parts of the urban areas are faced with the problem of floating fine particulate matter. Therefore, it is crucial to estimate the amounts of fine particulate matter concentrations through the urban atmosphere. In this research, an artificial neural network technique was utilized to model the PM2.5 dispersion in Tehran City. Factors which are influencing the predicted value consi...
متن کامل