Deep-learning GIS hybrid approach in precipitation modeling based on spatio-temporal variables in the coastal zone of Turkey

APAYDIN H., Sattari M. T.

CLIMATE RESEARCH, cilt.81, ss.149-165, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 81
  • Basım Tarihi: 2020
  • Doi Numarası: 10.3354/cr01612
  • Dergi Adı: CLIMATE RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, BIOSIS, CAB Abstracts, Environment Index, Geobase, Greenfile, Pollution Abstracts, Veterinary Science Database
  • Sayfa Sayıları: ss.149-165
  • Anahtar Kelimeler: Artificial intelligence, Precipitation, Spatio-temporal variables, Deep learning, East Mediterranean, Rainfall forecasting, LSTM, GPR, SVR, BFGS-ANN, M5, RF, ARTIFICIAL NEURAL-NETWORK, PREDICTION, INDEXES, REGION
  • Ankara Üniversitesi Adresli: Evet

Özet

It is clearly known that precipitation is essential for fauna and flora. Studies have shown that location and temporal factors have an effect on precipitation. Accurate prediction of precipitation is very important for water resource management, and artificial intelligence methods are frequently used to make such predictions. In this study, the deep-learning and geographic information system (GIS) hybrid approach based on spatio-temporal variables was applied in order to model the amount of precipitation on Turkey's coastline. Information about latitude, longitude, altitude, distance to the sea, and aspect was taken from meteorological stations, and these factors were utilized as spatial variables. The change in monthly precipitation was taken into account as a temporal variable. Artificial intelligence methods such as Gaussian process regression, support vector regression, the Broyden-Fletcher-Goldfarb-Shanno artificial neural network, M5, random forest, and long short-term memory (LSTM) were used. According to the results of the study, in which different input variable alternatives were also evaluated, LSTM was the most successful method for predicting precipitation with a value of 0.93 R. The study shows that the amount of precipitation can be estimated and a distribution map can be drawn by using spatio-temporal data and the deep-learning and GIS hybrid method at points where the measurement is not performed.