Hybrid Deep Learning Framework for Brain Tumor Classification Using MRI Images
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Accurate prediction of potential evapotranspiration (PET) is an important factor in irrigation planning, drought monitoring, and sustainable water resource management. This study presents a Narrative Review of the latest PET modeling studies, focusing on experimental models, artificial intelligence models, and optimized hybrid models. Sixteen studies were analyzed and classified into three main categories: optimized experimental models, standalone AI models, and optimized hybrid AI models. The results show that models such as ANN, RF, XGBoost, LSTM and DNN are able to represent non-linear relationships between climate variables and PET very efficiently. The integration of Meta-heuristic optimization algorithms with AI models also contributes to improving accuracy and fine-tuning of transactions. The study recommends focusing in the future on hybrid models, explainable artificial intelligence (XAI), and verification in multiple climates.
Accurate prediction of potential evapotranspiration (PET) is an important factor in irrigation planning, drought monitoring, and sustainable water resource management. This study presents a Narrative Review of the latest PET modeling studies, focusing on experimental models, artificial intelligence models, and optimized hybrid models. Sixteen studies were analyzed and classified into three main categories: optimized experimental models, standalone AI models, and optimized hybrid AI models. The results show that models such as ANN, RF, XGBoost, LSTM and DNN are able to represent non-linear relationships between climate variables and PET very efficiently. The integration of Meta-heuristic optimization algorithms with AI models also contributes to improving accuracy and fine-tuning of transactions. The study recommends focusing in the future on hybrid models, explainable artificial intelligence (XAI), and verification in multiple climates.
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