A Data Scientist's Guide to Streamflow Prediction
- URL: http://arxiv.org/abs/2006.12975v1
- Date: Fri, 5 Jun 2020 08:04:37 GMT
- Title: A Data Scientist's Guide to Streamflow Prediction
- Authors: Martin Gauch and Jimmy Lin
- Abstract summary: We focus on the element of hydrologic rainfall--runoff models and their application to forecast floods and predict streamflow.
This guide aims to help interested data scientists gain an understanding of the problem, the hydrologic concepts involved, and the details that come up along the way.
- Score: 55.22219308265945
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In recent years, the paradigms of data-driven science have become essential
components of physical sciences, particularly in geophysical disciplines such
as climatology. The field of hydrology is one of these disciplines where
machine learning and data-driven models have attracted significant attention.
This offers significant potential for data scientists' contributions to
hydrologic research. As in every interdisciplinary research effort, an initial
mutual understanding of the domain is key to successful work later on. In this
work, we focus on the element of hydrologic rainfall--runoff models and their
application to forecast floods and predict streamflow, the volume of water
flowing in a river. This guide aims to help interested data scientists gain an
understanding of the problem, the hydrologic concepts involved, and the details
that come up along the way. We have captured lessons that we have learned while
"coming up to speed" on streamflow prediction and hope that our experiences
will be useful to the community.
Related papers
- The Sound of Water: Inferring Physical Properties from Pouring Liquids [85.30865788636386]
We study the connection between audio-visual observations and the underlying physics of pouring liquids.
Our objective is to automatically infer physical properties such as the liquid level, the shape and size of the container, the pouring rate and the time to fill.
arXiv Detail & Related papers (2024-11-18T01:19:37Z) - TransGlow: Attention-augmented Transduction model based on Graph Neural
Networks for Water Flow Forecasting [4.915744683251151]
Hydrometric prediction of water quantity is useful for a variety of applications, including water management, flood forecasting, and flood control.
We propose atemporal forecasting model that augments the hidden state in Graph Convolution Recurrent Neural Network (GCRN) encoder-decoder.
We present a new benchmark dataset of water flow from a network of Canadian stations on rivers, streams, and lakes.
arXiv Detail & Related papers (2023-12-10T18:23:40Z) - An evaluation of deep learning models for predicting water depth
evolution in urban floods [59.31940764426359]
We compare different deep learning models for prediction of water depth at high spatial resolution.
Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model.
Our results show that the deep learning models present in general lower errors compared to the other methods.
arXiv Detail & Related papers (2023-02-20T16:08:54Z) - A Graph-Based Modeling Framework for Tracing Hydrological Pollutant
Transport in Surface Waters [0.0]
We present a graph modeling framework for understanding pollutant transport and fate across waterbodies, rivers, and watersheds.
The graph representation provides an intuitive approach for capturing connectivity and for identifying upstream pollutant sources.
Our tool ultimately seeks to help stakeholders design effective pollution prevention/mitigation practices.
arXiv Detail & Related papers (2023-02-10T00:30:38Z) - GFlowNets for AI-Driven Scientific Discovery [74.27219800878304]
We present a new probabilistic machine learning framework called GFlowNets.
GFlowNets can be applied in the modeling, hypotheses generation and experimental design stages of the experimental science loop.
We argue that GFlowNets can become a valuable tool for AI-driven scientific discovery.
arXiv Detail & Related papers (2023-02-01T17:29:43Z) - Learning-based estimation of in-situ wind speed from underwater
acoustics [58.293528982012255]
We introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics.
Our approach bridges data assimilation and learning-based frameworks to benefit both from prior physical knowledge and computational efficiency.
arXiv Detail & Related papers (2022-08-18T15:27:40Z) - Physics-informed Reinforcement Learning for Perception and Reasoning
about Fluids [0.0]
We propose a physics-informed reinforcement learning strategy for fluid perception and reasoning from observations.
We develop a method for the tracking (perception) and analysis (reasoning) of any previously unseen liquid whose free surface is observed with a commodity camera.
arXiv Detail & Related papers (2022-03-11T07:01:23Z) - Unlocking the potential of deep learning for marine ecology: overview,
applications, and outlook [8.3226670069051]
This paper aims to bridge the gap between marine ecologists and computer scientists.
We provide insight into popular deep learning approaches for ecological data analysis in plain language.
We illustrate challenges and opportunities through established and emerging applications of deep learning to marine ecology.
arXiv Detail & Related papers (2021-09-29T21:59:16Z) - Physics Guided Machine Learning Methods for Hydrology [21.410993515618895]
We propose an LSTM based deep learning architecture that is coupled with SWAT (Soil and Water Assessment Tool)
The efficacy of the approach is being analyzed on several small catchments located in the South Branch of the Root River Watershed in southeast Minnesota.
arXiv Detail & Related papers (2020-12-02T19:17:19Z) - Predictive Analytics for Water Asset Management: Machine Learning and
Survival Analysis [55.41644538483948]
We study a statistical and machine learning framework for the prediction of water pipe failures.
We use a dataset containing the failure records of all pipes within the water distribution network in Barcelona, Spain.
The results shed light on the effect of important risk factors, such as pipe geometry, age, material, and soil cover, among others.
arXiv Detail & Related papers (2020-07-02T19:08:36Z)
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.