Okan Mert Katipoğlu

ABSTRACT This study addresses a gap in irrigation water quality classification by comparing machine learning (ML) algorithms and deep learning (DL) models, with a focus on feature selection. Feature relevance was assessed using Hellinger distance, mutual information, and Chi-square analysis. Classifiers included boosting algorithms (AdaBoost, XGBoost, CatBoost, LightGBM, GradientBoosting, HistGradientBoosting), Extra Trees, and DL models (CNN-BiGRU and CNN-BiGRU-Attention).

Abstract Air temperature plays a critical role in estimating agricultural water requirements, hydrological processes, and the climate change impacts. This study aims to identify the most accurate forecasting model for daily minimum (Tmin) and maximum (Tmax) temperatures in a semi-arid environment.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.