Toward Routing River Water in Land Surface Models with Recurrent Neural Networks

• URL: http://arxiv.org/abs/2404.14212v4
• Date: Mon, 21 Oct 2024 13:34:22 GMT
• Title: Toward Routing River Water in Land Surface Models with Recurrent Neural Networks
• Authors: Mauricio Lima, Katherine Deck, Oliver R. A. Dunbar, Tapio Schneider,
• Abstract summary: We study the performance of recurrent neural networks (RNNs) for river routing in land surface models (LSMs) Instead of observed precipitation, the LSM-RNN uses instantaneous runoff calculated from physics-based models as an input. We compare the predictions from the LSM-RNN to an existing physics-based model with a similar dataset and find that the LSM-RNN outperforms the physics-based model.
• Score: 0.0
• License:
• Abstract: Machine learning is playing an increasing role in hydrology, supplementing or replacing physics-based models. One notable example is the use of recurrent neural networks (RNNs) for forecasting streamflow given observed precipitation and geographic characteristics. Training of such a model over the continental United States (CONUS) demonstrated that a single set of model parameters can be used across independent catchments, and that RNNs can outperform physics-based models. In this work, we take a next step and study the performance of RNNs for river routing in land surface models (LSMs). Instead of observed precipitation, the LSM-RNN uses instantaneous runoff calculated from physics-based models as an input. We train the model with data from river basins spanning the globe and test using historical streamflow measurements. The model demonstrates skill at generalization across basins (predicting streamflow in catchments not used in training) and across time (predicting streamflow during years not used in training). We compare the predictions from the LSM-RNN to an existing physics-based model calibrated with a similar dataset and find that the LSM-RNN outperforms the physics-based model. Our results show that RNNs are effective for global streamflow prediction from runoff inputs and motivate the development of complete routing models that can capture nested sub-basis connections.

Related papers

• Hierarchically Disentangled Recurrent Network for Factorizing System Dynamics of Multi-scale Systems [4.634606500665259]
  We present a knowledge-guided machine learning (KGML) framework for modeling multi-scale processes. We study its performance in the context of streamflow forecasting in hydrology.
  arXiv  Detail & Related papers  (2024-07-29T16:25:43Z)
• Physics-Inspired Deep Learning and Transferable Models for Bridge Scour Prediction [2.451326684641447]
  We introduce scour physics-inspired neural networks (SPINNs) for bridge scour prediction using deep learning. SPINNs integrate physics-based, empirical equations into deep neural networks and are trained using site-specific historical scour monitoring data. Despite variation in performance, SPINNs outperformed pure data-driven models in the majority of cases.
  arXiv  Detail & Related papers  (2024-07-01T13:08:09Z)
• Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks [1.8073031015436376]
  Physics-based models are computationally time-consuming and infeasible for real-time scenarios of urban drainage networks. Fully-connected neural networks (NNs) are potential surrogate models, but may suffer from low interpretability and efficiency in fitting complex targets. This work proposes a GNN-based surrogate of the flow routing model for the hydraulic prediction problem of drainage networks.
  arXiv  Detail & Related papers  (2024-04-16T07:08:04Z)
• Towards Theoretical Understandings of Self-Consuming Generative Models [56.84592466204185]
  This paper tackles the emerging challenge of training generative models within a self-consuming loop. We construct a theoretical framework to rigorously evaluate how this training procedure impacts the data distributions learned by future models. We present results for kernel density estimation, delivering nuanced insights such as the impact of mixed data training on error propagation.
  arXiv  Detail & Related papers  (2024-02-19T02:08:09Z)
• Towards Long-Term predictions of Turbulence using Neural Operators [68.8204255655161]
  It aims to develop reduced-order/surrogate models for turbulent flow simulations using Machine Learning. Different model structures are analyzed, with U-NET structures performing better than the standard FNO in accuracy and stability.
  arXiv  Detail & Related papers  (2023-07-25T14:09:53Z)
• Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting [72.89994745876086]
  This work presents the Online Neuro-Evolution-based Neural Architecture Search (ONE-NAS) algorithm. ONE-NAS is a novel neural architecture search method capable of automatically designing and dynamically training recurrent neural networks (RNNs) for online forecasting tasks. Results demonstrate that ONE-NAS outperforms traditional statistical time series forecasting methods.
  arXiv  Detail & Related papers  (2023-02-20T22:25:47Z)
• Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator [57.57321628587564]
  We introduce Hybrid Graph Network Simulator (HGNS) for learning reservoir simulations of 3D subsurface fluid flows. HGNS consists of a subsurface graph neural network (SGNN) to model the evolution of fluid flows, and a 3D-U-Net to model the evolution of pressure. Using an industry-standard subsurface flow dataset (SPE-10) with 1.1 million cells, we demonstrate that HGNS is able to reduce the inference time up to 18 times compared to standard subsurface simulators.
  arXiv  Detail & Related papers  (2022-06-15T17:29:57Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search [63.39818029362661]
  We present a model based on Neural Architecture Search (NAS) and self-learning for received signal strength ( RSS) map reconstruction. The approach first finds an optimal NN architecture and simultaneously train the deduced model over some ground-truth measurements of a given ( RSS) map. Experimental results show that signal predictions of this second model outperforms non-learning based state-of-the-art techniques and NN models with no architecture search.
  arXiv  Detail & Related papers  (2021-05-17T12:19:22Z)
• Handling Missing Observations with an RNN-based Prediction-Update Cycle [10.478312054103975]
  In tasks such as tracking, time-series data inevitably carry missing observations. This paper introduces an RNN-based approach that provides a full temporal filtering cycle for motion state estimation.
  arXiv  Detail & Related papers  (2021-03-22T11:55:10Z)
• High Temporal Resolution Rainfall Runoff Modelling Using Long-Short-Term-Memory (LSTM) Networks [0.03694429692322631]
  The model was tested for a watershed in Houston, TX, known for severe flood events. The LSTM network's capability in learning long-term dependencies between the input and output of the network allowed modeling RR with high resolution in time.
  arXiv  Detail & Related papers  (2020-02-07T00:38:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.