Non-monotonic Benefits of Spatial Detail in Deep Learning for Large-Sample Runoff Prediction

This repository contains the code for the paper "Non-monotonic Benefits of Spatial Detail in Deep Learning for Large-Sample Runoff Prediction". It enables the training and evaluation of four models, including Attr-LSTM, RasterMean-LSTM, MID-CNN-LSTM and HIGH-CNN-LSTM for rainfall-runoff simulations across 531 CAMELS watersheds.

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