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technical update to let the blog run under new hugo versios and some …
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danklotz committed Jan 18, 2024
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9 changes: 4 additions & 5 deletions about/index.html
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<description>Recent content in book chapter on Neural Hydrology</description>
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<title>NeuralHydrology-Interpreting LSTMs in Hydrology</title>
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<description>&lt;p&gt;Book chapter in the &lt;a href=&#34;https://link.springer.com/book/10.1007/978-3-030-28954-6&#34;&gt;Explainable AI: Interpreting, Explaining and Visualizing Deep Learning&lt;/a&gt; (Editors Wojciech SamekGrégoire MontavonAndrea VedaldiLars Kai HansenKlaus-Robert Müller).&lt;/p&gt;</description>
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<span> / </span>
<span class="author">
<a href="https://kratzert.github.io" title="Posts by Frederik Kratzert" rel="author">Frederik Kratzert</a>
<a href="/kratzert.github.io" title="Posts by Frederik Kratzert, Daniel Klotz" rel="author">Frederik Kratzert, Daniel Klotz</a>
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<description>&lt;p&gt;This paper performs a rigorous benchmark of traditional hydrologic models and an LSTM-based model for rainfall-runoff modeling.&lt;/p&gt;</description>
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<title>Hydrological Concept Formation inside Long Short-Term Memory (LSTM) networks </title>
<link>/post/research/lees2021concept/</link>
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<title>Caravan - A global community dataset for large-sample hydrology</title>
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<pubDate>Thu, 16 Jun 2022 20:23:59 +0530</pubDate>

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<description>&lt;p&gt;This paper introduces the &lt;a href=&#34;https://github.com/kratzert/Caravan/&#34;&gt;Caravan dataset&lt;/a&gt;, a global large-sample hydrology dataset that builds on cloud computing to be extensible by anyone.&lt;/p&gt;</description>
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<title>NeuralHydrology — A Python library for Deep Learning research in hydrology</title>
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<pubDate>Fri, 04 Mar 2022 20:23:59 +0530</pubDate>

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<description>&lt;p&gt;Accompanying paper to our open source Python library &lt;a href=&#34;https://github.com/neuralhydrology/neuralhydrology&#34;&gt;NeuralHydrology&lt;/a&gt;.&lt;/p&gt;</description>
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<title>On Strictly Enforced Mass Conservation Constraints for Modeling the Rainfall-Runoff Process</title>
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<pubDate>Thu, 20 Jan 2022 20:23:59 +0530</pubDate>

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<description>&lt;p&gt;This paper investigates the hypothesis that the lack of enforced mass conservation is the main reason that deep learning models outperform traditional hydrology models.&lt;/p&gt;</description>
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<item>
<title>Post processing the U.S. National Water Model with a Long Short-Term Memory network</title>
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<pubDate>Mon, 15 Nov 2021 20:23:59 +0530</pubDate>

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<description>&lt;p&gt;In this paper, we investigate the potential of using the LSTM as a post-processor for the US National Water Model.&lt;/p&gt;</description>
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<title>Technical Note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks</title>
<link>/post/research/nearing2021assimilation/</link>
<pubDate>Mon, 25 Oct 2021 20:23:59 +0530</pubDate>

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<description>&lt;p&gt;Technical note that compares autoregression to data assimilation for deep learning models and rainfall-runoff modeling.&lt;/p&gt;</description>
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<title>Deep learning rainfall-runoff predictions of extreme events</title>
<link>/post/research/frame2021extreme/</link>
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<description>&lt;p&gt;This paper investigates the hypothesis that deep learning models may not be reliable in extrapolating extreme events.&lt;/p&gt;</description>
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<title>A note on leveraging synergy in multiple meteorological datasets with deep learning for rainfall-runoff modeling</title>
<link>/post/research/kratzert2020multi/</link>
<pubDate>Thu, 20 May 2021 20:23:59 +0530</pubDate>

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<description>&lt;p&gt;In this paper we show the benefits of using multiple meteorological forcing products at the same time in a single LSTM-based rainfall-runoff model over just using a single product.&lt;/p&gt;</description>
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<title>Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network</title>
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<pubDate>Mon, 19 Apr 2021 08:23:59 +0530</pubDate>

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<description>&lt;p&gt;New LSTM-based architecture for predictions at multiple temporal time scales.&lt;/p&gt;</description>
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<title>Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling</title>
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<guid>/post/research/klotz2020uncertainty/</guid>
<description>&lt;p&gt;Deep learning based uncertainty estimation techniques and benchmarking procedure for rainfall-runoff modeling.&lt;/p&gt;</description>
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<title>MC-LSTM: Mass-Conserving LSTM</title>
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<pubDate>Thu, 14 Jan 2021 08:23:59 +0530</pubDate>

<guid>/post/research/hoedt2021mclstm/</guid>
<description>&lt;p&gt;In this study, we present a mass-conserving variant of the LSTM and its application to arithmetic tasks, traffic forecasting, modeling a pendulum and rainfall-runoff modeling.&lt;/p&gt;</description>
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<title>A Data Scientist&#39;s Guide to Streamflow Prediction</title>
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<pubDate>Fri, 05 Jun 2020 20:23:59 +0530</pubDate>

<guid>/post/research/gauch2020guide/</guid>
<description>&lt;p&gt;An introduction to hydrology and especially rainfall-runoff modeling, targeted at data scientists.&lt;/p&gt;</description>
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<item>
<title>Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning</title>
<link>/post/research/kratzert2019pub/</link>
<pubDate>Sat, 23 Nov 2019 20:23:59 +0530</pubDate>

<guid>/post/research/kratzert2019pub/</guid>
<description>&lt;p&gt;In this manuscript we test LSTM-based rainfall-runoff models on the task of prediction in ungauged basins and show, that a single LSTM-based model does better prediction in &lt;em&gt;ungauged&lt;/em&gt; basins than a traditional hydrological model that was specifically calibrated for each basin individually.&lt;/p&gt;</description>
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<item>
<title>The Proper Care and Feeding of CAMELS: How Limited Training Data Affects Streamflow Prediction</title>
<link>/post/research/gauch2020feeding/</link>
<pubDate>Sun, 17 Nov 2019 20:23:59 +0530</pubDate>

<guid>/post/research/gauch2020feeding/</guid>
<description>&lt;p&gt;This paper investigates the influence of the number of training basins and the training period length on the model performance for the EA-LSTM and XGBoost&lt;/p&gt;</description>
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<item>
<title>Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets</title>
<link>/post/research/kratzert2019regional/</link>
<pubDate>Fri, 02 Aug 2019 20:23:59 +0530</pubDate>

<guid>/post/research/kratzert2019regional/</guid>
<description>&lt;p&gt;In this manuscript we show for the first time how to train a single LSTM-based neural network as general hydrology model for hundreds of basins. Furthermore, we proposed the Entity-Aware LSTM (EA-LSTM) in which static features are used explicitly to subset the model for a specific entity (here a catchment).&lt;/p&gt;</description>
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<item>
<title>Do internals of neural networks make sense in the context of hydrology?</title>
<link>/post/research/kratzert2018agu/</link>
<pubDate>Mon, 10 Dec 2018 20:23:59 +0530</pubDate>

<guid>/post/research/kratzert2018agu/</guid>
<description>&lt;p&gt;Presentation at the AGU 2018 Fall Meeting on experiments regarding the interpretability of LSTM states.&lt;/p&gt;</description>
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