Prediction of maximum and minimum temperature of warm-water fish breeding pool using machine learning methods

اسعدی اسکویی, ابراهیم; Dehghan Jirdehi, Behzad; Sadidi Shal, Mohammad Taghi; Pakdaman, Morteza; Kianersi, Farahnaz

doi:10.22125/agmj.2024.410815.1157

Prediction of maximum and minimum temperature of warm-water fish breeding pool using machine learning methods

Document Type : Original Article

Authors

ابراهیم اسعدی اسکویی ¹

Behzad Dehghan Jirdehi ²

Mohammad Taghi Sadidi Shal ³

Morteza Pakdaman ¹

Farahnaz Kianersi ⁴

¹ Research Institute of Meteorological and Atmospheric Science (RIMAS), Climate Research Institute (CRI), Mashhad, Iran

² Expert of Gilan Agricultural Meteorology Research Center

³ Agricultural meteorological Research center of Guilan

⁴ South of Iran Aquaculture Research Institute, Iranian Fisheries Science Research Institute, Agricultural Research, Education, and Extension Organization (AREEO), Ahvaz, Iran

10.22125/agmj.2024.410815.1157

Abstract

Fish are cold blooded animals and their metabolism, growth and feeding are strongly dependent on water temperature. Temperature changes in fish breeding pools cause stress and disease outbreaks occur especially above the tolerance thresholds. The aim of this study is predicting pool water temperature from observed air temperate using several machine learning approaches, namely artificial neural network, gradient boosting and random forest in Gilan province.Maximum and minimum air temperature data of Rasht Agrometorological station for the period of June 2016 to November 2018 were collected and used for prediction of corresponding data of fish breeding pond .The obtained results showed that for prediction of the minimum temperature, the neural network model (with a root mean square of 1.93 and a correlation of 0.92) and for the pool water maximum temperature, the random forest model (with a root mean square of 1.61 and a correlation of 0.95) did a better job comparing to other two approaches. These selected models can be applied for prediction of water temperature using air Tmax and Tmin for improved management options under changing conditions.

Keywords

Artificial Neural Network

Boosting

Gradient

Machine Learning

Prediction

Random Forest

Warm-Water Fish

Subjects