26d388235d99826a6c2a5108ecb7d6f4  mathematics-13-03828-v2.pdf
aec89594a3454fda9b71c900d9312ddfb3b022f5  mathematics-13-03828-v2.pdf
58bfa4f3bcd1442c4be71db0a884f4f8f75439d4e78ad3287f38f82615b2d6e9  mathematics-13-03828-v2.pdf

Title:           Improving Individual and Regional Rainfall–Runoff Modeling in North American Watersheds Through Feature Selection and Hyperparameter Optimization
Subject:         Precise rainfall-runoff modeling (RRM) is vital for disaster management, resource conservation, and mitigation. Recent deep learning-based methods, such as long short-term memory (LSTM) networks, often struggle with major challenges, including temporal sensitivity, feature selection, generalizability, and hyperparameter tuning. The objective of this study is to develop an accurate and generalizable rainfall–runoff modeling framework that addresses the four aforementioned challenges. We propose a novel RRM framework that integrates transductive LSTM (TLSTM) to capture fine-grained temporal changes, off-policy proximal policy optimization (PPO) combined with Shapley Additive exPlanations (SHAP)-based reward functions for feature selection, an enhanced generative adversarial network (GAN) for online data augmentation, and Bayesian optimization hyperband (BOHB) for efficient hyperparameter tuning. TLSTM uses transductive learning, where samples near the test point are given extra weight, to capture fine-grained temporal shifts. Off-policy PPO contributes to this process by selecting features sensitive to temporal patterns in RRM. Our improved GAN conducts online data augmentation by excluding some gradients, increasing diversity and relevance in synthetic data. Finally, BOHB accelerates hyperparameter tuning by merging Bayesian optimization with the scaling efficiency of Hyperband. We evaluate our model using the Comprehensive Attributes and Meteorology for Large-Sample Studies (CAMELS) dataset under individual and regional scenarios. It achieves Nash–Sutcliffe efficiency (NSE) scores of 0.588 and 0.873, surpassing the baseline scores of 0.548 and 0.830, respectively. The generalizability of our approach was assessed on the hydro-climatic datasets for North America (HYSETS), also yielding improved performance. These improvements indicate more accurate capture of flow dynamics and peak events, supporting a robust and interpretable framework for RRM.
Keywords:        climate change; data augmentation; feature selection; hyperparameter  optimization; rainfall-runoff modeling; water resource management
Author:          Bahareh Ghanati and Joan Serra-Sagristà
Creator:         LaTeX with hyperref
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        Title: Evaluation Metrics 
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        Title: Model Overview 
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          Title: Online Data Augmentation 
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          Title: Feature Engineering and Prediction 
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    Title: Improving Individual and Regional Rainfall–Runoff Modeling in North American Watersheds Through Feature Selection and Hyperparameter Optimization
    Author: Bahareh Ghanati and Joan Serra-Sagristà
    Subject: Precise rainfall-runoff modeling (RRM) is vital for disaster management, resource conservation, and mitigation. Recent deep learning-based methods, such as long short-term memory (LSTM) networks, often struggle with major challenges, including temporal sensitivity, feature selection, generalizability, and hyperparameter tuning. The objective of this study is to develop an accurate and generalizable rainfall–runoff modeling framework that addresses the four aforementioned challenges. We propose a novel RRM framework that integrates transductive LSTM (TLSTM) to capture fine-grained temporal changes, off-policy proximal policy optimization (PPO) combined with Shapley Additive exPlanations (SHAP)-based reward functions for feature selection, an enhanced generative adversarial network (GAN) for online data augmentation, and Bayesian optimization hyperband (BOHB) for efficient hyperparameter tuning. TLSTM uses transductive learning, where samples near the test point are given extra weight, to capture fine-grained temporal shifts. Off-policy PPO contributes to this process by selecting features sensitive to temporal patterns in RRM. Our improved GAN conducts online data augmentation by excluding some gradients, increasing diversity and relevance in synthetic data. Finally, BOHB accelerates hyperparameter tuning by merging Bayesian optimization with the scaling efficiency of Hyperband. We evaluate our model using the Comprehensive Attributes and Meteorology for Large-Sample Studies (CAMELS) dataset under individual and regional scenarios. It achieves Nash–Sutcliffe efficiency (NSE) scores of 0.588 and 0.873, surpassing the baseline scores of 0.548 and 0.830, respectively. The generalizability of our approach was assessed on the hydro-climatic datasets for North America (HYSETS), also yielding improved performance. These improvements indicate more accurate capture of flow dynamics and peak events, supporting a robust and interpretable framework for RRM.
    Keywords: climate change; data augmentation; feature selection; hyperparameter  optimization; rainfall-runoff modeling; water resource management
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