Sensory Precipitation Forecast Using Artificial Neural Networks and Decision Trees
Meteorology stations sold in the market have various difficulties in terms of their use, also these systems are costly to obtain. With state of the art sensor technologies, the development of mini weather stations has become easier. This study focuses on the development of a model weather station de...
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| Format: | Article |
| Language: | English |
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Çanakkale Onsekiz Mart University
2022-06-01
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| Series: | Journal of Advanced Research in Natural and Applied Sciences |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/1931253 |
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| author | Ünal Kızıl Hakkı Fırat Altınbilek Sefa Aksu Hakan Nar |
| author_facet | Ünal Kızıl Hakkı Fırat Altınbilek Sefa Aksu Hakan Nar |
| author_sort | Ünal Kızıl |
| collection | DOAJ |
| description | Meteorology stations sold in the market have various difficulties in terms of their use, also these systems are costly to obtain. With state of the art sensor technologies, the development of mini weather stations has become easier. This study focuses on the development of a model weather station device using temperature, relative humidity, UV, LDR Light, rain and soil moisture sensors to collect major environmental data. The measured data were wirelessly transmitted to the remote station for logging via the GSM module and the information was sent to the database in the internet environment. In addition, the data from the sensors are organized by correlation. The classification was made according to the data obtained from the rain sensor and the relationship between the other 5 sensors used in the device to the rain classification was examined. Sensor data were scaled between 0-1 with min-max normalization before being subjected to deep learning and machine learning training. In the Decision Tree (DT) a model score of 0.96 was obtained by choosing the maximum depth of 20. The artificial neural network (ANN) yielded a classification score of 0.92 using 4 hidden layers and 100 epochs in the artificial neural network model. |
| format | Article |
| id | doaj-art-1bfa10a0c4804f278adf7d0df62c80ab |
| institution | Kabale University |
| issn | 2757-5195 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Çanakkale Onsekiz Mart University |
| record_format | Article |
| series | Journal of Advanced Research in Natural and Applied Sciences |
| spelling | doaj-art-1bfa10a0c4804f278adf7d0df62c80ab2025-02-05T17:58:10ZengÇanakkale Onsekiz Mart UniversityJournal of Advanced Research in Natural and Applied Sciences2757-51952022-06-018230932110.28979/jarnas.984312453Sensory Precipitation Forecast Using Artificial Neural Networks and Decision TreesÜnal Kızıl0https://orcid.org/0000-0002-8512-3899Hakkı Fırat Altınbilek1https://orcid.org/0000-0001-6761-1445Sefa Aksu2https://orcid.org/0000-0002-2348-4082Hakan Nar3https://orcid.org/0000-0002-5354-6379ÇANAKKALE ONSEKİZ MART ÜNİVERSİTESİ, ZİRAAT FAKÜLTESİ, TARIMSAL YAPILAR VE SULAMA BÖLÜMÜÇANAKKALE ONSEKİZ MART ÜNİVERSİTESİ, ZİRAAT FAKÜLTESİ, TARIMSAL YAPILAR VE SULAMA BÖLÜMÜ, TARIMSAL YAPILAR VE SULAMA PR.ÇANAKKALE ONSEKİZ MART ÜNİVERSİTESİ, ZİRAAT FAKÜLTESİ, TARIMSAL YAPILAR VE SULAMA BÖLÜMÜ, TARIMSAL YAPILAR VE SULAMA PR.ÇANAKKALE ONSEKİZ MART ÜNİVERSİTESİ, ZİRAAT FAKÜLTESİ, TARIMSAL YAPILAR VE SULAMA BÖLÜMÜ, TARIMSAL YAPILAR VE SULAMA PR.Meteorology stations sold in the market have various difficulties in terms of their use, also these systems are costly to obtain. With state of the art sensor technologies, the development of mini weather stations has become easier. This study focuses on the development of a model weather station device using temperature, relative humidity, UV, LDR Light, rain and soil moisture sensors to collect major environmental data. The measured data were wirelessly transmitted to the remote station for logging via the GSM module and the information was sent to the database in the internet environment. In addition, the data from the sensors are organized by correlation. The classification was made according to the data obtained from the rain sensor and the relationship between the other 5 sensors used in the device to the rain classification was examined. Sensor data were scaled between 0-1 with min-max normalization before being subjected to deep learning and machine learning training. In the Decision Tree (DT) a model score of 0.96 was obtained by choosing the maximum depth of 20. The artificial neural network (ANN) yielded a classification score of 0.92 using 4 hidden layers and 100 epochs in the artificial neural network model.https://dergipark.org.tr/en/download/article-file/1931253agricultural sensorsenvironmental controlmachine learningprecipitation forecastweather station |
| spellingShingle | Ünal Kızıl Hakkı Fırat Altınbilek Sefa Aksu Hakan Nar Sensory Precipitation Forecast Using Artificial Neural Networks and Decision Trees Journal of Advanced Research in Natural and Applied Sciences agricultural sensors environmental control machine learning precipitation forecast weather station |
| title | Sensory Precipitation Forecast Using Artificial Neural Networks and Decision Trees |
| title_full | Sensory Precipitation Forecast Using Artificial Neural Networks and Decision Trees |
| title_fullStr | Sensory Precipitation Forecast Using Artificial Neural Networks and Decision Trees |
| title_full_unstemmed | Sensory Precipitation Forecast Using Artificial Neural Networks and Decision Trees |
| title_short | Sensory Precipitation Forecast Using Artificial Neural Networks and Decision Trees |
| title_sort | sensory precipitation forecast using artificial neural networks and decision trees |
| topic | agricultural sensors environmental control machine learning precipitation forecast weather station |
| url | https://dergipark.org.tr/en/download/article-file/1931253 |
| work_keys_str_mv | AT unalkızıl sensoryprecipitationforecastusingartificialneuralnetworksanddecisiontrees AT hakkıfırataltınbilek sensoryprecipitationforecastusingartificialneuralnetworksanddecisiontrees AT sefaaksu sensoryprecipitationforecastusingartificialneuralnetworksanddecisiontrees AT hakannar sensoryprecipitationforecastusingartificialneuralnetworksanddecisiontrees |