Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models

Ecological Informatics, Год журнала: 2024, Номер 85, С. 102933 - 102933

Опубликована: Дек. 7, 2024

Язык: Английский

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Estimation of crop evapotranspiration using statistical and machine learning techniques with limited meteorological data: a case study in Udham Singh Nagar, India

Theoretical and Applied Climatology, Год журнала: 2024, Номер 155(6), С. 5279 - 5296

Опубликована: Апрель 3, 2024

Язык: Английский

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Hybrid catboost models optimized with metaheuristics for predicting shear strength in rock joints

Bulletin of Engineering Geology and the Environment, Год журнала: 2025, Номер 84(3)

Опубликована: Фев. 25, 2025

Язык: Английский

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Revolutionizing the Future of Hydrological Science: Impact of Machine Learning and Deep Learning amidst Emerging Explainable AI and Transfer Learning

Applied Computing and Geosciences, Год журнала: 2024, Номер 24, С. 100206 - 100206

Опубликована: Ноя. 9, 2024

Язык: Английский

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Principal Component Analysis (PCA) and feature importance-based dimension reduction for Reference Evapotranspiration (ET0) predictions of Taif, Saudi Arabia

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 222, С. 109036 - 109036

Опубликована: Май 21, 2024

Язык: Английский

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Development of machine learning models for estimation of daily evaporation and mean temperature: a case study in New Delhi, India

Water Practice & Technology, Год журнала: 2024, Номер 19(7), С. 2655 - 2672

Опубликована: Июнь 4, 2024

ABSTRACT Accurate prediction of pan evaporation and mean temperature is crucial for effective water resources management, influencing the hydrological cycle impacting availability. This study focused on New Delhi's semi-arid climate, data spanning 31 years (1990–2020) were used to predict these variables using advanced algorithms such as Bagging, Random Subspace (RSS), M5P, REPTree. The models rigorously evaluated 10 performance metrics, including correlation coefficient, absolute error (MAE), Nash–Sutcliffe Efficiency (NSE) model coefficient. Bagging emerged best with indices values r, MAE, RMSE, RAE, RRSE, MBE NSE, d, KGE, MAPE 0.86, 0.76, 1.43, 32.70, 49.44, 0.03, 0.85, 0.96, 0.90, 22.0, respectively, during testing phase prediction. In predicting temperature, reported results 0.90 phase. These findings offer valuable insights enhancing relative humidity in diverse climatic conditions. model's robust underscores its potential application resource management.

Язык: Английский

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Hyperspectral indices data fusion-based machine learning enhanced by MRMR algorithm for estimating maize chlorophyll content

Frontiers in Plant Science, Год журнала: 2024, Номер 15

Опубликована: Окт. 16, 2024

Accurate estimation of chlorophyll is essential for monitoring maize health and growth, which hyperspectral imaging provides rich data. In this context, paper presents an innovative method to estimate by combining indices advanced machine learning models. The methodology study focuses on the development models using proprietary corn content. Six were used, including robust linear stepwise regression, support vector machines (SVM), fine Gaussian SVM, Matern 5/2 three-layer neural network. MRMR algorithm was integrated into process improve feature selection identifying most informative spectral bands, thereby reducing data redundancy improving model performance. results showed significant differences in performance six applied estimation. Among models, regression highest prediction accuracy. achieved R 2 = 0.71 training set, RMSE 338.46 µg/g MAE 264.30 µg/g. case validation further improved its performance, reaching =0.79, RMSE=296.37 µg/g, MAE=237.12 These metrics show that Matern’s combined with select optimal traits highly effective predicting This research has important implications precision agriculture, particularly real-time management crop health. allows farmers take timely targeted action.

Язык: Английский

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Evaluating statistical and machine learning techniques for sugarcane yield forecasting in the tarai region of North India

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 229, С. 109667 - 109667

Опубликована: Дек. 9, 2024

Язык: Английский

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Evaporation forecasting using different machine learning models in Beni Haroun Dam, Algeria

Theoretical and Applied Climatology, Год журнала: 2025, Номер 156(2)

Опубликована: Янв. 18, 2025

Язык: Английский

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Optimizing water management and climate-resilient agriculture in rice-fallow regions of the Dwarakeswar river basin using ML models

Deleted Journal, Год журнала: 2025, Номер 7(4)

Опубликована: Апрель 11, 2025

Язык: Английский

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