Modeling stochastic systems is a real challenge in many areas. Even the meteorology domain is not an exception; the modeling of precipitation activity is markedly stochastic and is influenced by a number of related physical variables (temperature, pressure, humidity, wind). Accurate precipitation estimation is thus highly non-trivial. Today's technical capability, automated data measuring (whether using Radar or automatic meteorological stations), as well as subsequent large-scale data processing and regression model training, allow the meteorological estimations and predictions with increasing accuracy. This paper demonstrates selected uses of artificial neural networks in the field of meteorology, as well as solving problems with pre-processing and integrating time-spatial meteorological data.
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Short-Term Rainfall Estimation by Machine Learning Methods
Semantic Scholar · Computer Science · 2019
Abstract
Modeling stochastic systems is a real challenge in many areas. Even the meteorology domain is not an exception; the modeling of precipitation activity is markedly stochastic and is influenced by a number of related physical variables (temperature, pressure, humidity, wind). Accurate precipitation estimation is thus highly non-trivial. Today's technical capability, automated data measuring (whether using Radar or automatic meteorological stations), as well as subsequent large-scale data processing and regression model training, allow the meteorological estimations and predictions with increasing accuracy. This paper demonstrates selected uses of artificial neural networks in the field of meteorology, as well as solving problems with pre-processing and integrating time-spatial meteorological data.