A Parsimonious Setup for Streamflow Forecasting using CNN-LSTM

no code implementations • 11 Apr 2024 • Sudan Pokharel, Tirthankar Roy

Significant strides have been made in advancing streamflow predictions, notably with the introduction of cutting-edge machine-learning models.

Time Series

An Autoencoder-based Snow Drought Index

no code implementations • 23 May 2023 • Sinan Rasiya Koya, Kanak Kanti Kar, Shivendra Srivastava, Tsegaye Tadesse, Mark Svoboda, Tirthankar Roy

We use reanalysis data (NLDAS-2) from 1981 to 2021 for the Pacific United States to study the efficacy of the new snow drought index.

Self-Supervised Learning

Temporal Fusion Transformers for Streamflow Prediction: Value of Combining Attention with Recurrence

no code implementations • 21 May 2023 • Sinan Rasiya Koya, Tirthankar Roy

Over the past few decades, the hydrology community has witnessed notable advancements in streamflow prediction, particularly with the introduction of cutting-edge machine-learning algorithms.

Differentiable modeling to unify machine learning and physical models and advance Geosciences

no code implementations • 10 Jan 2023 • Chaopeng Shen, Alison P. Appling, Pierre Gentine, Toshiyuki Bandai, Hoshin Gupta, Alexandre Tartakovsky, Marco Baity-Jesi, Fabrizio Fenicia, Daniel Kifer, Li Li, Xiaofeng Liu, Wei Ren, Yi Zheng, Ciaran J. Harman, Martyn Clark, Matthew Farthing, Dapeng Feng, Praveen Kumar, Doaa Aboelyazeed, Farshid Rahmani, Hylke E. Beck, Tadd Bindas, Dipankar Dwivedi, Kuai Fang, Marvin Höge, Chris Rackauckas, Tirthankar Roy, Chonggang Xu, Binayak Mohanty, Kathryn Lawson

Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and ushering in a paradigm shift.