Opis

• Tytuł:
  Rainfall Prediction using Artificial Neural Network in Semi-Arid mountainous region, Saudi Arabia
• Twórca:
  Khan, Roohul Abad;
  El Morabet, Rachida;
  Mallick, Javed;
  Azam, Mohammed;
  Vambol, Viola;
  Vambol, Sergij;
  Sydorenko, Volodymyr
• Opis:
  Rainfall prediction using Artificial Intelligence technique is gaining attention nowadays. Semi-arid region receives rainfall below potential evapotranspiration but more than arid region. However, in mountainous semi-arid region high rainfall intensity makes it highly variable. This renders rainfall prediction difficult by applying normal techniques and calls for data pre-processing. This study presents rainfall prediction in semi-arid mountainous region of Abha, KSA. The study adopted Moving Average (Method) for data pre-processing based on 2 years, 3 years, 4 years, 5 years and 10 years. The Artificial Neural Network (ANN) was trained for a period of 1978-2016 rainfall data. The neural network was validated against the existing data of period 1997-2006. The trained neural network was used to predict for period of 2017-2025. The performance of the model was evaluated against AAE, MAE, RMSE, MASE and PP. The mean absolute error was observed least in 2 years moving average model. However, the most accurate prediction models were obtained from 2 years moving average and 5 year moving average. The study concludes that ANN coupled with MA have potential of predicting rainfall in Semi-Arid mountainous region.
• Dostępność obiektu:
  Dostęp otwarty
• Prawa: https://creativecommons.org/licenses/by-nd/4.0
• Data:
  2021.09.24
• Typ:
  info:eu-repo/semantics/article;
  info:eu-repo/semantics/publishedVersion
• Język:
  angielski
• Źródło:
  Ecological Questions; Vol. 32 No. 4 (2021); 127-133;
  Ecological Questions; Tom 32 Nr 4 (2021); 127-133;
  1644-7298
• Format:
  application/pdf
• Wydawca:
  Uniwersytet Mikołaja Kopernika w Toruniu
• Relacja: https://apcz.umk.pl/EQ/article/view/35463/29835
• Temat i słowa kluczowe:
  moving average method;
  data pre-processing;
  mean absolute error;
  root mean square error;
  mean absolute scaled error;
  performance parameter
• Identyfikator: https://apcz.umk.pl/EQ/article/view/35463
• Dostawca danych:
  Akademicka Platforma Czasopism
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