Rain Intensity Forecast with Microcontroller Based Pluviometer and Machine Learning

Creative Commons License

Esirge Z., Beyaz A.

BRAZILIAN ARCHIVES OF BIOLOGY AND TECHNOLOGY, cilt.65, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 65
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1590/1678-4324-2022220197
  • Dergi Adı: BRAZILIAN ARCHIVES OF BIOLOGY AND TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Animal Behavior Abstracts, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Veterinary Science Database, Directory of Open Access Journals
  • Anahtar Kelimeler: pluviometer, rainfall intensity, microcontroller-based control, ultrasonic sensor, machine learning
  • Ankara Üniversitesi Adresli: Evet

Özet

One of the most important problems today is global warming, which occurs because of negative changes in the ecosystem. Global warming manifests itself as an increase in temperature, also a decrease in the number of glaciers, additionally an increase in seawater level, and irregularities in the rainfall. Correct measures should be taken to get rid of the resulting rainfall irregularities with the least damage. More efficient use of water resources can only be achieved through data analysis. In agriculture, the amount of rain falling on the field per unit of time is critical. The research focused on the amount of rain falling per unit time and a new mobile pluviometer was developed for this aim. With the designed microcontroller-based pluviometer, the rainfall intensity was determined, and after that data analysis was made with machine learning. In the developed pluviometer, firstly, the calibration process was performed, and then the rainfall intensity measurements were taken successfully. When these studies are considered, usage opportunities arise in subjects such as measuring the intensity of rainfall, efficient use of water, and taking precautions against natural disasters. Using machine learning techniques, Decision Tree, Random Forests, and Naive Bayes, the rainfall intensity forecasted between 98.5 to 100 % accuracy within the area of the investigation.