Nearest neighbour models for local and regional avalanche forecasting

نویسنده

  • M. Gassner
چکیده

This paper presents two avalanche forecasting applications NXD2000 and NXD-REG which were developed at the Swiss Federal Institute for Snow and Avalanche Research (SLF). Even both are based on the nearest neighbour method they are targeted to different scales. NXD2000 is used to forecast avalanches on a local scale. It is operated by avalanche forecasters responsible for snow safety at snow sport areas, villages or cross country roads. The area covered ranges from 10 km2 up to 100 km2 depending on the climatological homogeneity. It provides the forecaster with ten most similar days to a given situation. The observed avalanches of these days are an indication of the actual avalanche danger. NXD-REG is used operationally by the Swiss avalanche warning service for regional avalanche forecasting. The Nearest Neighbour approach is applied to the data sets of 60 observer stations. The results of each station are then compiled into a map of current and future avalanche hazard. Evaluation of the model by cross-validation has shown that the model can reproduce the official SLF avalanche forecasts in about 52% of the days.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Nearest Neighbour Based Forecast Model for PM10 Forecasting: Individual and Combination Forecasting

Air quality forecasting using nearest neighbour technique provides an alternative to statistical and neural network models, which needs the information on predictor variables and understanding of underlying patterns in the data. k-nearest neighbour method of forecasting that does not assume any linear or nonlinear form of the data is used in this study to obtain the next step forecast of PM10 c...

متن کامل

Fuzzy Nearest Neighbour Method for Time-series Forecasting

This paper explores a nearest neighbour pattern recognition method for time-series forecasting. A nearest neighbour method (FNNM) based on fuzzy membership values is developed. The main aim of the forecasting algorithm is to make single point forecasts into the future on the basis of past nearest neighbours. The nearest neighbours are selected using a membership threshold value. The results inc...

متن کامل

Forecasting using a Fuzzy Nearest Neighbour Method

1 Singh, S. "Forecasting using a Fuzzy Nearest Neighbour Method", Proc. 6th International Conference on Fuzzy Theory and Technology , Fourth Joint Conference on Information Sciences (JCIS'98), North Carolina, vol. 1, pp.80-83, 1998 (23-28 October ,1998) ABSTRACT This paper explores a nearest neighbour pattern recognition method for time-series forecasting. A nearest neighbour method (FNNM) base...

متن کامل

Artificial Neural Networks for Snow Avalanche Forecasting in Indian Himalaya

ASTRACT: Snow avalanches pose serious threat to Indian troops deployed in snow-bound areas of western Himalaya during winter months. The most viable way to mitigate avalanche threat in these areas is to precisely predict the time and place of their occurrences. Since, the factors involved in the formation of an avalanche are too many and underlying physical processes are quite complex, no predi...

متن کامل

Evaluating the Performance of Nearest Neighbour Algorithms when Forecasting US Industry Returns

Using both industry-specific data on 55 US industry sectors and an extensive range of macroeconomic variables, the authors compare the performance of nearest neighbour algorithms, OLS, and a number of two-stage models based on these two methods, when forecasting industry returns. As industry returns are a relatively under-researched area in the Finance literature, we also give a brief review of...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2001