AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A novel hybrid framework, integrating seven advanced filter-based and embedded feature selection techniques with an Extreme Gradient Boosting (XGBoost) model, to enhance reference evapotranspiration (ETo) estimation, revealed promising results for ETo estimation for both climatic conditions.
Precision irrigation has emerged as a critical adaptation strategy for alleviating global water scarcity under climate change, relying heavily on the accurate estimation of reference evapotranspiration (ET 0 ). Although physically based models, such as the FAO-56 Penman-Monteith equation, serve as the global standa...
Overall, ET0 provides substantial atmospheric-demand information for ETa prediction, whereas Rs assumes the dominant predictive role, and SM provides particularly important complementary information when ET0 is unavailable, demonstrating the importance of considering predictor composition together with model structure...
Reference evapotranspiration (ETo) is a fundamental parameter for irrigation scheduling and efficient water resources management. This study aimed to compare the performance of Linear Regression (LR), second-order Polynomial Regression (PR), Decision Tree (DT), Random Forest (RF), Support Vector Regression (SVR), and A...
This study proposes a hybrid machine learning framework to predict the six-month Standardized Precipitation Index (SPI₆) for meteorological drought assessment in Nanded, India, using NASA POWER data and demonstrates that ensemble learning enhances SPI prediction accuracy.
The findings demonstrate the potential of combining convolution-based feature extraction with attention-based modeling for accurate one-month-ahead monthly ET0 forecasting under the climatic conditions investigated in this study.
A combined geospatial and machine learning system to estimate stage-based paddy yield stress during Kuruvai 2023 in the Thanjavur region using vegetation indices and agrometeorological variables revealed that reproductive-stage humidity, rainfall, and ripening-stage soil moisture were the most important yield drivers.
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