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<?xml version="1.0" encoding="utf-8" standalone="yes"?>
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<channel>
<title>Neural Hydrology</title>
<link>/</link>
<description>Recent content on Neural Hydrology</description>
<generator>Hugo</generator>
<language>en-us</language>
<lastBuildDate>Thu, 12 Dec 2024 17:23:59 +0530</lastBuildDate>
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<item>
<title>Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell</title>
<link>/post/research/espinoza2024multifreq/</link>
<pubDate>Thu, 12 Dec 2024 17:23:59 +0530</pubDate>
<guid>/post/research/espinoza2024multifreq/</guid>
<description><p>Another approach for handling multiple frequencies in a single LSTM based model.</p></description>
</item>
<item>
<title>Caravan MultiMet: Extending Caravan with Multiple Weather Nowcasts and Forecasts</title>
<link>/post/datasets/shalev2024multimet/</link>
<pubDate>Thu, 14 Nov 2024 20:23:59 +0530</pubDate>
<guid>/post/datasets/shalev2024multimet/</guid>
<description><p>Additional hindcast/nowcast and weather forecast features for all gauges in Caravan.</p></description>
</item>
<item>
<title>A data-centric perspective on the information needed for hydrological uncertainty predictions</title>
<link>/post/research/auer2024hopcpt/</link>
<pubDate>Thu, 12 Sep 2024 17:23:59 +0530</pubDate>
<guid>/post/research/auer2024hopcpt/</guid>
<description><p>We use the framework of conformal prediction to investigate the impact of temporal and spatial information on uncertainty estimates within hydrological predictions.</p></description>
</item>
<item>
<title>HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin</title>
<link>/post/research/kratzert2024never/</link>
<pubDate>Thu, 12 Sep 2024 17:23:59 +0530</pubDate>
<guid>/post/research/kratzert2024never/</guid>
<description><p>Never, never, (really never!?) train an LSTM on a single basin. Or should you?</p></description>
</item>
<item>
<title>Technical Note: The divide and measure nonconformity – how metrics can mislead when we evaluate on different data partitions</title>
<link>/post/research/klotz2024damn/</link>
<pubDate>Tue, 13 Aug 2024 17:23:59 +0530</pubDate>
<guid>/post/research/klotz2024damn/</guid>
<description><p>The evaluation of model performance is an essential part of hydrological modeling. However &hellip;</p></description>
</item>
<item>
<title>Analyzing the generalization capabilities of hybrid hydrological models for extrapolation to extreme events</title>
<link>/post/research/espinoza2024extreme/</link>
<pubDate>Tue, 30 Jul 2024 17:23:59 +0530</pubDate>
<guid>/post/research/espinoza2024extreme/</guid>
<description><p>Comparing LSTMs to hybrid deep learning models on generalization capabilities during extreme events.</p></description>
</item>
<item>
<title>Generalizing Tree–Level Sap Flow Across the European Continent</title>
<link>/post/research/loritz2024sapflow/</link>
<pubDate>Sun, 14 Apr 2024 17:23:59 +0530</pubDate>
<guid>/post/research/loritz2024sapflow/</guid>
<description><p>Research study investigating the regional LSTM approach for sap flow prediction.</p></description>
</item>
<item>
<title>Global prediction of extreme floods in ungauged watersheds</title>
<link>/post/research/nearing2024nature/</link>
<pubDate>Wed, 03 Apr 2024 17:23:59 +0530</pubDate>
<guid>/post/research/nearing2024nature/</guid>
<description><p>Benchmarking study of Google&rsquo;s operational Flood Forecasting model compared to GloFAS.</p></description>
</item>
<item>
<title>In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance</title>
<link>/post/research/gauch2022metrics/</link>
<pubDate>Thu, 25 May 2023 17:23:59 +0530</pubDate>
<guid>/post/research/gauch2022metrics/</guid>
<description><p>In this paper, we present the results of the &ldquo;Rate My Hydrograph&rdquo; study, where we compare expert ratings of simulated hydrographs with quantitative metrics.</p></description>
</item>
<item>
<title>The persistence of errors: How evaluating models over data partitions relates to a global evaluation</title>
<link>/post/research/klotz2023egu/</link>
<pubDate>Sun, 07 May 2023 17:10:51 +0530</pubDate>
<guid>/post/research/klotz2023egu/</guid>
<description><p>Oral presentation for EGU General Assembly 2023. This is one about a certain phenomena that appears when we evaluate a model over subsets of the data. Eventually the plan is to make a technical note out of it.</p></description>
</item>
<item>
<title>Flood forecasting with machine learning models in an operational framework </title>
<link>/post/research/nevo2021operational/</link>
<pubDate>Fri, 05 Aug 2022 20:23:59 +0530</pubDate>
<guid>/post/research/nevo2021operational/</guid>
<description><p>In this paper, we present the full operational framework used by the Flood Forecasting team at Google.</p></description>
</item>
<item>
<title>The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL)</title>
<link>/post/research/mai2022grip/</link>
<pubDate>Fri, 08 Jul 2022 20:23:59 +0530</pubDate>
<guid>/post/research/mai2022grip/</guid>
<description><p>This paper performs a rigorous benchmark of traditional hydrologic models and an LSTM-based model for rainfall-runoff modeling.</p></description>
</item>
<item>
<title>Hydrological Concept Formation inside Long Short-Term Memory (LSTM) networks </title>
<link>/post/research/lees2021concept/</link>
<pubDate>Mon, 20 Jun 2022 20:23:59 +0530</pubDate>
<guid>/post/research/lees2021concept/</guid>
<description><p>In this paper, we investigate what information the LSTM captures about the hydrological system.</p></description>
</item>
<item>
<title>Caravan - A global community dataset for large-sample hydrology</title>
<link>/post/research/kratzert2022caravan/</link>
<pubDate>Thu, 16 Jun 2022 20:23:59 +0530</pubDate>
<guid>/post/research/kratzert2022caravan/</guid>
<description><p>This paper introduces the <a href="https://github.com/kratzert/Caravan/">Caravan dataset</a>, a global large-sample hydrology dataset that builds on cloud computing to be extensible by anyone.</p></description>
</item>
<item>
<title>Rate my Hydrograph: Evaluating the Conformity of Expert Judgment and Quantitative Metrics</title>
<link>/post/research/gauch2022egu/</link>
<pubDate>Mon, 23 May 2022 17:23:59 +0530</pubDate>
<guid>/post/research/gauch2022egu/</guid>
<description><p>Oral presentation at the EGU General Assembly 2022 on a social study to compare expert rankings of simulated hydrographs with quantitative metrics.</p></description>
</item>
<item>
<title>Deficiencies in Hydrological Modelling Practices</title>
<link>/post/research/klotz2022egu/</link>
<pubDate>Sat, 07 May 2022 17:10:51 +0530</pubDate>
<guid>/post/research/klotz2022egu/</guid>
<description><p>Oral presentation for EGU General Assembly 2022. This is where my model evaluation journey started.</p></description>
</item>
<item>
<title>NeuralHydrology — A Python library for Deep Learning research in hydrology</title>
<link>/post/research/kratzert2022joss/</link>
<pubDate>Fri, 04 Mar 2022 20:23:59 +0530</pubDate>
<guid>/post/research/kratzert2022joss/</guid>
<description><p>Accompanying paper to our open source Python library <a href="https://github.com/neuralhydrology/neuralhydrology">NeuralHydrology</a>.</p></description>
</item>
<item>
<title>On Strictly Enforced Mass Conservation Constraints for Modeling the Rainfall-Runoff Process</title>
<link>/post/research/frame2022mass/</link>
<pubDate>Thu, 20 Jan 2022 20:23:59 +0530</pubDate>
<guid>/post/research/frame2022mass/</guid>
<description><p>This paper investigates the hypothesis that the lack of enforced mass conservation is the main reason that deep learning models outperform traditional hydrology models.</p></description>
</item>
<item>
<title>Forward vs. Inverse Methods for Using Near-Real-Time Streamflow Observation Data in Long Short-Term Memory Networks</title>
<link>/post/research/nearing2022agu/</link>
<pubDate>Mon, 13 Dec 2021 20:23:59 +0530</pubDate>
<guid>/post/research/nearing2022agu/</guid>
<description><p>Presentation at the AGU 2021 Fall Meeting presenting approaches to improve LSTM streamflow predictions with near-real-time observation data.</p></description>
</item>
<item>
<title>Post processing the U.S. National Water Model with a Long Short-Term Memory network</title>
<link>/post/research/frame2020postprocessing/</link>
<pubDate>Mon, 15 Nov 2021 20:23:59 +0530</pubDate>
<guid>/post/research/frame2020postprocessing/</guid>
<description><p>In this paper, we investigate the potential of using the LSTM as a post-processor for the US National Water Model.</p></description>
</item>
<item>
<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>
<guid>/post/research/nearing2021assimilation/</guid>
<description><p>Technical note that compares autoregression to data assimilation for deep learning models and rainfall-runoff modeling.</p></description>
</item>
<item>
<title>Deep learning rainfall-runoff predictions of extreme events</title>
<link>/post/research/frame2021extreme/</link>
<pubDate>Wed, 18 Aug 2021 20:23:59 +0530</pubDate>
<guid>/post/research/frame2021extreme/</guid>
<description><p>This paper investigates the hypothesis that deep learning models may not be reliable in extrapolating extreme events.</p></description>
</item>
<item>
<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>
<guid>/post/research/kratzert2020multi/</guid>
<description><p>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.</p></description>
</item>
<item>
<title>Multi-Timescale LSTM for Rainfall–Runoff Forecasting</title>
<link>/post/research/gauch2021egu/</link>
<pubDate>Fri, 07 May 2021 17:23:59 +0530</pubDate>
<guid>/post/research/gauch2021egu/</guid>
<description><p>Oral presentation at the virtual EGU General Assembly 2021 on rainfall–runoff forecasting with Multi-Timescale LSTM.</p></description>
</item>
<item>
<title>Large-scale river network modeling using Graph Neural Networks</title>
<link>/post/research/kratzert2021egu/</link>
<pubDate>Fri, 07 May 2021 17:20:55 +0530</pubDate>
<guid>/post/research/kratzert2021egu/</guid>
<description><p>Oral presentation at the virtual EGU General Assembly 2021 on rainfall–runoff prediction with Graph Neural Networks.</p></description>
</item>
<item>
<title>Uncertainty estimation with LSTM based rainfall-runoff models</title>
<link>/post/research/klotz2021egu/</link>
<pubDate>Fri, 07 May 2021 17:10:51 +0530</pubDate>
<guid>/post/research/klotz2021egu/</guid>
<description><p>Oral presentation at the virtual EGU General Assembly 2021 on uncertainty prediction with LSTMs in the context of rainfall-runoff modeling.</p></description>
</item>
<item>
<title>Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network</title>
<link>/post/research/gauch2020mtslstm/</link>
<pubDate>Mon, 19 Apr 2021 08:23:59 +0530</pubDate>
<guid>/post/research/gauch2020mtslstm/</guid>
<description><p>New LSTM-based architecture for predictions at multiple temporal time scales.</p></description>
</item>
<item>
<title>Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling</title>
<link>/post/research/klotz2020uncertainty/</link>
<pubDate>Wed, 14 Apr 2021 15:23:59 +0530</pubDate>
<guid>/post/research/klotz2020uncertainty/</guid>
<description><p>Deep learning based uncertainty estimation techniques and benchmarking procedure for rainfall-runoff modeling.</p></description>
</item>
<item>
<title>CAMELS US: Hourly USGS discharge observations and NLDAS forcings</title>
<link>/post/datasets/camels-us-hourly-nldas-and-streamflow/</link>
<pubDate>Sat, 16 Jan 2021 20:23:59 +0530</pubDate>
<guid>/post/datasets/camels-us-hourly-nldas-and-streamflow/</guid>
<description><p>This dataset contains hourly discharge observations and NLDAS forcings for 516 CAMELS US basins.</p></description>
</item>
<item>
<title>LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe</title>
<link>/post/datasets/lamah-ce/</link>
<pubDate>Sat, 16 Jan 2021 20:23:59 +0530</pubDate>
<guid>/post/datasets/lamah-ce/</guid>
<description><p>LamaH-CE contains a collection of runoff and meteorological time series as well as various (catchment) attributes for 859 gauged basins in the upper Danube catchment and Austria.</p></description>
</item>
<item>
<title>MC-LSTM: Mass-Conserving LSTM</title>
<link>/post/research/hoedt2021mclstm/</link>
<pubDate>Thu, 14 Jan 2021 08:23:59 +0530</pubDate>
<guid>/post/research/hoedt2021mclstm/</guid>
<description><p>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.</p></description>
</item>
<item>
<title>Examining the uncertainty estimation properties of LSTM based rainfall-runoff models</title>
<link>/post/research/klotz2020agu/</link>
<pubDate>Thu, 17 Dec 2020 20:23:59 +0530</pubDate>
<guid>/post/research/klotz2020agu/</guid>
<description><p>Oral presentation at the virtual AGU Fall Meeting 2020 on uncertainty prediction with LSTMs in the context of rainfall-runoff modeling.</p></description>
</item>
<item>
<title>LSTM-Based Rainfall–Runoff Modeling at Arbitrary Time Scales</title>
<link>/post/research/gauch2020agu/</link>
<pubDate>Thu, 17 Dec 2020 20:23:59 +0530</pubDate>
<guid>/post/research/gauch2020agu/</guid>
<description><p>Oral presentation at the virtual AGU Fall Meeting 2020 on streamflow prediction at arbitraty timescales with a single LSTM-based model.</p></description>
</item>
<item>
<title>A Deep Learning Architecture for Conservative Dynamical Systems: Application to Rainfall-Runoff Modeling</title>
<link>/post/research/nearing2020neurips/</link>
<pubDate>Sun, 06 Dec 2020 20:23:59 +0530</pubDate>
<guid>/post/research/nearing2020neurips/</guid>
<description><p>Spotlight talk at the AI for Earth Sciences workshop of the NeurIPS 2020, presenting a first glimpse of a new LSTM-based model that conserves mass by design: the Mass-Conserving LSTM</p></description>
</item>
<item>
<title>A Machine Learner's Guide to Streamflow Prediction</title>
<link>/post/research/gauch2020neurips/</link>
<pubDate>Sun, 06 Dec 2020 20:23:59 +0530</pubDate>
<guid>/post/research/gauch2020neurips/</guid>
<description><p>Spotlight talk at the AI for Earth Sciences workshop of the NeurIPS 2020, presenting introduction of the world and terminology of hydrology/streamflow prediction for data scientists.</p></description>
</item>
<item>
<title>What Role Does Hydrological Science Play in the Age of Machine Learning?</title>
<link>/post/research/nearing2020opinion/</link>
<pubDate>Fri, 13 Nov 2020 20:23:59 +0530</pubDate>
<guid>/post/research/nearing2020opinion/</guid>
<description><p>Opinion paper discussing the future of Hydrology, especially in the context of recent developments in Machine Learning</p></description>
</item>
<item>
<title>A Data Scientist's Guide to Streamflow Prediction</title>
<link>/post/research/gauch2020guide/</link>
<pubDate>Fri, 05 Jun 2020 20:23:59 +0530</pubDate>
<guid>/post/research/gauch2020guide/</guid>
<description><p>An introduction to hydrology and especially rainfall-runoff modeling, targeted at data scientists.</p></description>
</item>
<item>
<title>HydroNets: Leveraging River Network Structure and Deep Neural Networks for Hydrologic Modeling</title>
<link>/post/research/moshe2020egu/</link>
<pubDate>Mon, 04 May 2020 20:23:59 +0530</pubDate>
<guid>/post/research/moshe2020egu/</guid>
<description><p>Virtual presentation at the EGU General Assembly 2020 presenting the HydroNets architecture for modeling entire river networks.</p></description>
</item>
<item>
<title>Learning from mistakes: Online updating for deep learning models</title>
<link>/post/research/klotz2020egu/</link>
<pubDate>Mon, 04 May 2020 20:23:59 +0530</pubDate>
<guid>/post/research/klotz2020egu/</guid>
<description><p>Virtual presentation at the EGU General Assembly 2020 on data assimilation with LSTM-based models.</p></description>
</item>
<item>
<title>The performance of LSTM models from basin to continental scales</title>
<link>/post/research/kratzert2020egu1/</link>
<pubDate>Mon, 04 May 2020 20:23:59 +0530</pubDate>
<guid>/post/research/kratzert2020egu1/</guid>
<description><p>Virtual presentation at the EGU General Assembly 2020 comparing LSTMs trained for each basin individually with a single LSTM trained for all basins together.</p></description>
</item>
<item>
<title>Towards deep learning based flood forecasting for ungauged basins</title>
<link>/post/research/kratzert2020egu2/</link>
<pubDate>Mon, 04 May 2020 20:23:59 +0530</pubDate>
<guid>/post/research/kratzert2020egu2/</guid>
<description><p>Invited talk at the EGU General Assembly 2020 on using LSTMs for flood forecasting in ungauged basins.</p></description>
</item>
<item>
<title>HydroNets: Leveraging River Structure for Hydrologic Modeling</title>
<link>/post/research/moshe2020iclr/</link>
<pubDate>Sun, 26 Apr 2020 20:23:59 +0530</pubDate>
<guid>/post/research/moshe2020iclr/</guid>
<description><p>Modeling entire nested river trees by integrating the river hierachy into the neural network architecture. This manuscripts proposes HydroNets, an architecture designed for modeling multiple nested gauge stations.</p></description>
</item>
<item>
<title>Streamflow Prediction with Limited Spatially-Distributed Input Data</title>
<link>/post/research/gauch2019neurips/</link>
<pubDate>Wed, 11 Dec 2019 20:23:59 +0530</pubDate>
<guid>/post/research/gauch2019neurips/</guid>
<description><p>Workshop paper, investigating first ways of using LSTM-based models for climate change related questions in hydrology.</p></description>
</item>
<item>
<title>Using LSTMs for climate change assessment studies on droughts and floods</title>
<link>/post/research/kratzert2019neurips/</link>
<pubDate>Wed, 11 Dec 2019 20:23:59 +0530</pubDate>
<guid>/post/research/kratzert2019neurips/</guid>
<description><p>Workshop paper, investigating first ways of using LSTM-based models for climate change related questions in hydrology.</p></description>
</item>
<item>
<title>Large-Scale Rainfall-Runoff Modeling using the Long Short-Term Memory Network</title>
<link>/post/research/kratzert2019agu/</link>
<pubDate>Tue, 10 Dec 2019 20:23:59 +0530</pubDate>
<guid>/post/research/kratzert2019agu/</guid>
<description><p>Presentation at the AGU 2019 Fall Meeting presenting our recent results on large-scale hydrological modeling using LSTMs.</p></description>
</item>
<item>
<title>Machine Learning for Streamflow Prediction: Current Status and Future Prospects</title>
<link>/post/research/gauch2019agu/</link>
<pubDate>Tue, 10 Dec 2019 20:23:59 +0530</pubDate>
<guid>/post/research/gauch2019agu/</guid>
<description><p>Poster at the AGU 2019 Fall Meeting presenting Martins work on a model comparison study for the Great Lakes/Lake Erie area.</p></description>
</item>
<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><p>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 <em>ungauged</em> basins than a traditional hydrological model that was specifically calibrated for each basin individually.</p></description>
</item>
<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><p>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</p></description>
</item>
<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><p>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).</p></description>
</item>
<item>
<title>Using large data sets towards generating a catchment aware hydrological model for global applications</title>
<link>/post/research/kratzert2019egu/</link>
<pubDate>Wed, 10 Apr 2019 20:23:59 +0530</pubDate>
<guid>/post/research/kratzert2019egu/</guid>
<description><p>PICO presentation at the EGU General Assembly 2019 on prediction in ungauged basins using LSTM based models.</p></description>
</item>
<item>
<title>Towards the quantification of uncertainty for deep learning based rainfall-runoff models</title>
<link>/post/research/klotz2019egu/</link>
<pubDate>Mon, 08 Apr 2019 20:23:59 +0530</pubDate>
<guid>/post/research/klotz2019egu/</guid>
<description><p>Poster presentation at the EGU General Assembly 2019 on uncertainty estimation using MC-Dropout and LSTMs.</p></description>
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<item>
<title>Long Short-Term Memory (LSTM) networks for rainfall-runoff modeling</title>
<link>/post/research/kratzert2019cuahsi/</link>
<pubDate>Fri, 05 Apr 2019 20:23:59 +0530</pubDate>
<guid>/post/research/kratzert2019cuahsi/</guid>
<description><p>Video presentation in CUAHSI&rsquo;s 2019 Spring Cyberseminar Series on Recent advances in big data machine learning in Hydrology.</p></description>
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<item>
<title>NeuralHydrology-Interpreting LSTMs in Hydrology</title>
<link>/post/research/kratzert2019interpretability/</link>
<pubDate>Tue, 19 Mar 2019 20:23:59 +0530</pubDate>
<guid>/post/research/kratzert2019interpretability/</guid>
<description><p>Book chapter in the <a href="https://link.springer.com/book/10.1007/978-3-030-28954-6">Explainable AI: Interpreting, Explaining and Visualizing Deep Learning</a> (Editors Wojciech SamekGrégoire MontavonAndrea VedaldiLars Kai HansenKlaus-Robert Müller).</p></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><p>Presentation at the AGU 2018 Fall Meeting on experiments regarding the interpretability of LSTM states.</p></description>
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<item>
<title>A glimpse into the Unobserved: Runoff simulation for ungauged catchments with LSTMs</title>
<link>/post/research/kratzert2018glimpse/</link>
<pubDate>Fri, 07 Dec 2018 20:23:59 +0530</pubDate>
<guid>/post/research/kratzert2018glimpse/</guid>
<description><p>NeurIPS 2018 workshop paper, showing first results on using LSTMs for prediction in ungauged basins.</p></description>
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<item>
<title>Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks</title>
<link>/post/research/kratzert2018lstm/</link>
<pubDate>Thu, 22 Nov 2018 20:23:59 +0530</pubDate>
<guid>/post/research/kratzert2018lstm/</guid>
<description><p>First publication using LSTMs as rainfall-runoff model.</p></description>
</item>
<item>
<title>About</title>
<link>/about/</link>
<pubDate>Wed, 14 Aug 2019 21:05:33 +0530</pubDate>
<guid>/about/</guid>
<description><h4 id="about-neuralhydrology"><strong>About NeuralHydrology</strong></h4>
<p>Everything started with the idea of using LSTMs as a general rainfall-runoff model, back in 2016. Daniel and myself (Frederik) were self-studying machine learning and trying to keep up with the fast-paced developments of that field. At that time, both of us were still working at the <a href="https://boku.ac.at/wau/hywa">Institute for Hydrology and Watermangement</a> (former Institute of Water Management, Hydrology and Hydraulic Engineering), Daniel as PhD-student and I as a student assistant. A lot of our free-time went into designing and conducting first experiments, to see if the LSTM is able to model the rainfall-runoff relationship at all. By then, we jokingly referred to this idea as <em>neural hydrology</em>, not knowing that <a href="https://rgs-ibg.onlinelibrary.wiley.com/doi/10.1111/j.1475-4762.1999.tb00179.x">Bob Abrahart (1999)</a> coined a similar term (<em>NeuroHydrology</em>) already 20 years ago. In his paper, Bob Abrahart advocates the use of neural networks in hydrology, while still referring to classical multi layer perceptrons and not recurrent neural networks.</p></description>
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<title>Python Library: neuralHydrology</title>
<link>/library/</link>
<pubDate>Wed, 14 Aug 2019 21:05:33 +0530</pubDate>
<guid>/library/</guid>
<description><p><img src="/img/neural-hyd-logo.gif#center" alt="Logo"></p>
<p>Python library to train neural networks with a strong focus on hydrological applications.</p>
<ul>
<li>GitHub: <a href="https://github.com/neuralhydrology/neuralhydrology">https://github.com/neuralhydrology/neuralhydrology</a></li>
<li>Documentation: <a href="https://neuralhydrology.readthedocs.io">neuralhydrology.readthedocs.io</a></li>
<li>Bug reports/Feature requests <a href="https://github.com/neuralhydrology/neuralhydrology/issues">https://github.com/neuralhydrology/neuralhydrology/issues</a></li>
</ul>
<p>This package has been used extensively in research over the last year and was used in various academic publications.
The core idea of this package is modularity in all places to allow easy integration of new datasets, new model
architectures or any training related aspects (e.g. loss functions, optimizer, regularization).
One of the core concepts of this code base are configuration files, which lets anyone train neural networks without
touching the code itself. The <code>neuralHydrology</code> package is build on top of the deep learning framework
<a href="https://pytorch.org/">Pytorch</a>, since it has proven to be the most flexible and useful for research purposes.</p></description>
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