Uncertainty Quantification in Inverse Models in Hydrology
- URL: http://arxiv.org/abs/2310.02193v1
- Date: Tue, 3 Oct 2023 16:39:21 GMT
- Title: Uncertainty Quantification in Inverse Models in Hydrology
- Authors: Somya Sharma Chatterjee, Rahul Ghosh, Arvind Renganathan, Xiang Li,
Snigdhansu Chatterjee, John Nieber, Christopher Duffy, Vipin Kumar
- Abstract summary: We propose a knowledge-guided, probabilistic inverse modeling method for recovering physical characteristics from streamflow and weather data.
We compare our framework with state-of-the-art inverse models for estimating river basin characteristics.
Our framework also offers improved explainability since it can quantify uncertainty in both the inverse and the forward model.
- Score: 9.020366051310384
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In hydrology, modeling streamflow remains a challenging task due to the
limited availability of basin characteristics information such as soil geology
and geomorphology. These characteristics may be noisy due to measurement errors
or may be missing altogether. To overcome this challenge, we propose a
knowledge-guided, probabilistic inverse modeling method for recovering physical
characteristics from streamflow and weather data, which are more readily
available. We compare our framework with state-of-the-art inverse models for
estimating river basin characteristics. We also show that these estimates offer
improvement in streamflow modeling as opposed to using the original basin
characteristic values. Our inverse model offers 3\% improvement in R$^2$ for
the inverse model (basin characteristic estimation) and 6\% for the forward
model (streamflow prediction). Our framework also offers improved
explainability since it can quantify uncertainty in both the inverse and the
forward model. Uncertainty quantification plays a pivotal role in improving the
explainability of machine learning models by providing additional insights into
the reliability and limitations of model predictions. In our analysis, we
assess the quality of the uncertainty estimates. Compared to baseline
uncertainty quantification methods, our framework offers 10\% improvement in
the dispersion of epistemic uncertainty and 13\% improvement in coverage rate.
This information can help stakeholders understand the level of uncertainty
associated with the predictions and provide a more comprehensive view of the
potential outcomes.
Related papers
- eXplainable Bayesian Multi-Perspective Generative Retrieval [6.823521786512908]
We introduce uncertainty calibration and interpretability into a retrieval pipeline.
We incorporate techniques such as LIME and SHAP to analyze the behavior of a black-box reranker model.
Our methods demonstrate substantial performance improvements across three KILT datasets.
arXiv Detail & Related papers (2024-02-04T09:34:13Z) - Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation [50.920911532133154]
The intrinsic ill-posedness and ordinal-sensitive nature of monocular depth estimation (MDE) models pose major challenges to the estimation of uncertainty degree.
We propose to model the uncertainty of MDE models from the perspective of the inherent probability distributions.
By simply introducing additional training regularization terms, our model, with surprisingly simple formations and without requiring extra modules or multiple inferences, can provide uncertainty estimations with state-of-the-art reliability.
arXiv Detail & Related papers (2023-07-19T12:11:15Z) - Toward Reliable Human Pose Forecasting with Uncertainty [51.628234388046195]
We develop an open-source library for human pose forecasting, including multiple models, supporting several datasets.
We devise two types of uncertainty in the problem to increase performance and convey better trust.
arXiv Detail & Related papers (2023-04-13T17:56:08Z) - Reliable Multimodal Trajectory Prediction via Error Aligned Uncertainty
Optimization [11.456242421204298]
In a well-calibrated model, uncertainty estimates should perfectly correlate with model error.
We propose a novel error aligned uncertainty optimization method and introduce a trainable loss function to guide the models to yield good quality uncertainty estimates aligning with the model error.
We demonstrate that our method improves average displacement error by 1.69% and 4.69%, and the uncertainty correlation with model error by 17.22% and 19.13% as quantified by Pearson correlation coefficient on two state-of-the-art baselines.
arXiv Detail & Related papers (2022-12-09T12:33:26Z) - The Implicit Delta Method [61.36121543728134]
In this paper, we propose an alternative, the implicit delta method, which works by infinitesimally regularizing the training loss of uncertainty.
We show that the change in the evaluation due to regularization is consistent for the variance of the evaluation estimator, even when the infinitesimal change is approximated by a finite difference.
arXiv Detail & Related papers (2022-11-11T19:34:17Z) - Probabilistic Inverse Modeling: An Application in Hydrology [5.221546270391291]
We propose a probabilistic inverse model framework that can reconstruct robust hydrology basin characteristics.
We address two aspects of building more explainable inverse models, uncertainty estimation and robustness.
arXiv Detail & Related papers (2022-10-12T14:00:37Z) - Probabilistic Deep Learning to Quantify Uncertainty in Air Quality
Forecasting [5.007231239800297]
This work applies state-of-the-art techniques of uncertainty quantification in a real-world setting of air quality forecasts.
We describe training probabilistic models and evaluate their predictive uncertainties based on empirical performance, reliability of confidence estimate, and practical applicability.
Our experiments demonstrate that the proposed models perform better than previous works in quantifying uncertainty in data-driven air quality forecasts.
arXiv Detail & Related papers (2021-12-05T17:01:18Z) - Dense Uncertainty Estimation [62.23555922631451]
In this paper, we investigate neural networks and uncertainty estimation techniques to achieve both accurate deterministic prediction and reliable uncertainty estimation.
We work on two types of uncertainty estimations solutions, namely ensemble based methods and generative model based methods, and explain their pros and cons while using them in fully/semi/weakly-supervised framework.
arXiv Detail & Related papers (2021-10-13T01:23:48Z) - Knowledge-guided Self-supervised Learning for estimating River-Basin
Characteristics [21.692056111111476]
We present an inverse model that uses a knowledge-guided self-supervised learning algorithm to infer basin characteristics.
We evaluate our model on the the CAMELS dataset and the results validate its ability to reduce measurement uncertainty.
arXiv Detail & Related papers (2021-09-14T04:38:23Z) - Back2Future: Leveraging Backfill Dynamics for Improving Real-time
Predictions in Future [73.03458424369657]
In real-time forecasting in public health, data collection is a non-trivial and demanding task.
'Backfill' phenomenon and its effect on model performance has been barely studied in the prior literature.
We formulate a novel problem and neural framework Back2Future that aims to refine a given model's predictions in real-time.
arXiv Detail & Related papers (2021-06-08T14:48:20Z) - Learning Interpretable Deep State Space Model for Probabilistic Time
Series Forecasting [98.57851612518758]
Probabilistic time series forecasting involves estimating the distribution of future based on its history.
We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks.
We show in experiments that our model produces accurate and sharp probabilistic forecasts.
arXiv Detail & Related papers (2021-01-31T06:49:33Z)
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.