Artificial neural network approach for monthly air temperature estimations and maps

Abstract

Accurate air temperature estimation plays a crucial role in weather planning, and hydrological modeling techniques including water supply, drought analysis, and demand problems of a country. In this study, monthly maximum air temperature (Tmax), minimum air temperature (Tmin), and mean air temperature (T) values for T??rkiye were modeled using the Feed-forward neural network (FNN) and Elman neural network (ENN) approaches. In the developed models, monthly Tmax, Tmin, and T values at 81 measurement stations over the period of 1927 through 2021 recorded by the Turkish State Meteorological Service (known as MGM in T??rkiye) were used. While the periodicity (month of the year) and geographical variables (latitude, longitude, and altitude) were used as inputs in the developed models, the Tmax, Tmin, and T variables were defined as the output. First of all, long-term monthly air temperature maps were created with the help of the ArcGIS software program using the observed values. Then, maps of long-term air temperatures were created with the ArcGIS software program using the predicted values of the developed artificial neural network models. To prove the accuracy of the models, the predicted long-term air temperature maps were compared with those actual maps. The obtained results showed that the long-term monthly air temperature maps of the studied regions could be created with a small and acceptable error as a function of geographical information and periodicity component. By creating artificial intelligence maps in this way, it will be possible to accurately predict the long-term monthly air temperatures as a function of latitude, longitude, altitude, and the number of months at any point where no measurement is made.