# An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions
## 3 Materials and Methods
### Overview of the Available Datasets and Selection of a Training Dataset  
The dataset from the meteorological station with code 205 located at Szczecin was taken from the archives of Institute of Meteorology and Water Management (IMGW) ([[https://dane.imgw.pl/data/dane_pomiarowo_observacyjne/dane_meteorologiczne/dobowe/synop/](https://dane.imgw.pl/data/dane_pomiarowo_observacyjne/dane_meteorologiczne/dobowe/synop/)]([https://dane.imgw.pl/data/dane_pomiarowo_observacyjne/dane_meteorologiczne/dobowe/synop/](https://dane.imgw.pl/data/dane_pomiarowo_observacyjne/dane_meteorologiczne/dobowe/synop/)) accessed on 27 April 2021). This source of meteorological data was considered reliable due to the status of IMGW as a state research unit.The real-time forecasting of meteorological phenomena is one of the basic tasks of modern meteorological services. In Poland, official weather forecasts are prepared and developed by the Institute of Meteorology and Water Management-National Research Institute (IMiGW-PIB)--a state unit supervised by the minister in charge of water management. The mission of IMiGW-PIB is to inform society and organizations about the weather--meteorological and hydrological, climate change, and all factors influencing the current weather in Poland. The major task of the IMGW-PIB is to provide meteorological cover for Poland. For this purpose, the IMGW-PIB Monitor was created--a service for all national operational services and administrative bodies. As part of its statutory activities, the IMGW-PIB prepares and delivers meteorological forecasts, warnings against dangerous phenomena occurring in the atmosphere, and dedicated announcements and bulletins. IMGW is responsible for collecting, storing, processing, and making available domestic and foreign measurements and observation materials. The Institute develops and distributes weather forecasts and warnings. IMGW is a member of many international organizations. It represents Poland on the forum of WMO (World Meteorological Organisation) or Eumetsat (European Organisation for the Exploitation of Meteorological Satellites). IMGW stores, and makes available in its database, measurement and observation data collected since 1960 for 2112 measuring stations in Poland ([[https://danepubliczne.imgw.pl/data/dane_pomiarowo_observacyjne/dane_meteorologiczne/](https://danepubliczne.imgw.pl/data/dane_pomiarowo_observacyjne/dane_meteorologiczne/)]([https://danepubliczne.imgw.pl/data/dane_pomiarowo_observacyjne/dane_meteorologiczne/](https://danepubliczne.imgw.pl/data/dane_pomiarowo_observacyjne/dane_meteorologiczne/)) accessed on 27 April 2021). A review of the literature on the use of multilayer neural networks in the prediction of selected weather conditions demonstrated that, for many studies, ten years of previous meteorological data were used [74; 75]. We, therefore, decided to use meteorological data from 10 years (2011-2020). In this study, daily data from 7 years (2011-2017) were used as a training dataset with 10 training features for the 3 days before the day for which the forecast was performed. The test dataset included data from 3 years (2018-2020). One sample contained selected weather conditions for each day. Due to the many samples in the dataset, we chose to apply a simple validation (train/test split) to check the effectiveness of the model. The dimensions of the datasets used for model training and testing are presented in Table 1.  
These variables from 1, 2, and 3 days before the prediction date were the inputs for the MLP model. The time horizon of 3-5 days before the day for the forecast was used, among others, by [PERSON] and others in a study published in 2020 [76]. Table 3 contains the conditions planned for forecasting using the MLP model in this study. They are the outputs of the MLP model. We selected five weather conditions that are interesting for people from the point of view of watching the weather forecast and are most often provided in weather forecasts, i.e., the maximum and minimum temperature, pressure, wind speed, and precipitation.