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6 | 6 | <description>Recent content in code on Neural Hydrology</description> |
7 | 7 | <generator>Hugo -- gohugo.io</generator> |
8 | 8 | <language>en-us</language> |
9 | | - <lastBuildDate>Wed, 19 Oct 2022 17:23:59 +0530</lastBuildDate><atom:link href="/categories/code/index.xml" rel="self" type="application/rss+xml" /> |
| 9 | + <lastBuildDate>Wed, 19 Oct 2022 17:23:59 +0530</lastBuildDate> |
| 10 | + <atom:link href="/categories/code/index.xml" rel="self" type="application/rss+xml" /> |
10 | 11 | <item> |
11 | 12 | <title>In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance</title> |
12 | 13 | <link>/post/research/gauch2022metrics/</link> |
13 | 14 | <pubDate>Wed, 19 Oct 2022 17:23:59 +0530</pubDate> |
14 | | - |
15 | 15 | <guid>/post/research/gauch2022metrics/</guid> |
16 | 16 | <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> |
17 | 17 | </item> |
18 | | - |
19 | 18 | <item> |
20 | 19 | <title>The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL)</title> |
21 | 20 | <link>/post/research/mai2022grip/</link> |
22 | 21 | <pubDate>Fri, 08 Jul 2022 20:23:59 +0530</pubDate> |
23 | | - |
24 | 22 | <guid>/post/research/mai2022grip/</guid> |
25 | 23 | <description><p>This paper performs a rigorous benchmark of traditional hydrologic models and an LSTM-based model for rainfall-runoff modeling.</p></description> |
26 | 24 | </item> |
27 | | - |
28 | 25 | <item> |
29 | 26 | <title>Hydrological Concept Formation inside Long Short-Term Memory (LSTM) networks </title> |
30 | 27 | <link>/post/research/lees2021concept/</link> |
31 | 28 | <pubDate>Mon, 20 Jun 2022 20:23:59 +0530</pubDate> |
32 | | - |
33 | 29 | <guid>/post/research/lees2021concept/</guid> |
34 | 30 | <description><p>In this paper, we investigate what information the LSTM captures about the hydrological system.</p></description> |
35 | 31 | </item> |
36 | | - |
37 | 32 | <item> |
38 | 33 | <title>Caravan - A global community dataset for large-sample hydrology</title> |
39 | 34 | <link>/post/research/kratzert2022caravan/</link> |
40 | 35 | <pubDate>Thu, 16 Jun 2022 20:23:59 +0530</pubDate> |
41 | | - |
42 | 36 | <guid>/post/research/kratzert2022caravan/</guid> |
43 | 37 | <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> |
44 | 38 | </item> |
45 | | - |
46 | 39 | <item> |
47 | 40 | <title>NeuralHydrology — A Python library for Deep Learning research in hydrology</title> |
48 | 41 | <link>/post/research/kratzert2022joss/</link> |
49 | 42 | <pubDate>Fri, 04 Mar 2022 20:23:59 +0530</pubDate> |
50 | | - |
51 | 43 | <guid>/post/research/kratzert2022joss/</guid> |
52 | 44 | <description><p>Accompanying paper to our open source Python library <a href="https://github.com/neuralhydrology/neuralhydrology">NeuralHydrology</a>.</p></description> |
53 | 45 | </item> |
54 | | - |
55 | 46 | <item> |
56 | 47 | <title>On Strictly Enforced Mass Conservation Constraints for Modeling the Rainfall-Runoff Process</title> |
57 | 48 | <link>/post/research/frame2022mass/</link> |
58 | 49 | <pubDate>Thu, 20 Jan 2022 20:23:59 +0530</pubDate> |
59 | | - |
60 | 50 | <guid>/post/research/frame2022mass/</guid> |
61 | 51 | <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> |
62 | 52 | </item> |
63 | | - |
64 | 53 | <item> |
65 | 54 | <title>Post processing the U.S. National Water Model with a Long Short-Term Memory network</title> |
66 | 55 | <link>/post/research/frame2020postprocessing/</link> |
67 | 56 | <pubDate>Mon, 15 Nov 2021 20:23:59 +0530</pubDate> |
68 | | - |
69 | 57 | <guid>/post/research/frame2020postprocessing/</guid> |
70 | 58 | <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> |
71 | 59 | </item> |
72 | | - |
73 | 60 | <item> |
74 | 61 | <title>Technical Note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks</title> |
75 | 62 | <link>/post/research/nearing2021assimilation/</link> |
76 | 63 | <pubDate>Mon, 25 Oct 2021 20:23:59 +0530</pubDate> |
77 | | - |
78 | 64 | <guid>/post/research/nearing2021assimilation/</guid> |
79 | 65 | <description><p>Technical note that compares autoregression to data assimilation for deep learning models and rainfall-runoff modeling.</p></description> |
80 | 66 | </item> |
81 | | - |
82 | 67 | <item> |
83 | 68 | <title>Deep learning rainfall-runoff predictions of extreme events</title> |
84 | 69 | <link>/post/research/frame2021extreme/</link> |
85 | 70 | <pubDate>Wed, 18 Aug 2021 20:23:59 +0530</pubDate> |
86 | | - |
87 | 71 | <guid>/post/research/frame2021extreme/</guid> |
88 | 72 | <description><p>This paper investigates the hypothesis that deep learning models may not be reliable in extrapolating extreme events.</p></description> |
89 | 73 | </item> |
90 | | - |
91 | 74 | <item> |
92 | 75 | <title>A note on leveraging synergy in multiple meteorological datasets with deep learning for rainfall-runoff modeling</title> |
93 | 76 | <link>/post/research/kratzert2020multi/</link> |
94 | 77 | <pubDate>Thu, 20 May 2021 20:23:59 +0530</pubDate> |
95 | | - |
96 | 78 | <guid>/post/research/kratzert2020multi/</guid> |
97 | 79 | <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> |
98 | 80 | </item> |
99 | | - |
100 | 81 | <item> |
101 | 82 | <title>Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network</title> |
102 | 83 | <link>/post/research/gauch2020mtslstm/</link> |
103 | 84 | <pubDate>Mon, 19 Apr 2021 08:23:59 +0530</pubDate> |
104 | | - |
105 | 85 | <guid>/post/research/gauch2020mtslstm/</guid> |
106 | 86 | <description><p>New LSTM-based architecture for predictions at multiple temporal time scales.</p></description> |
107 | 87 | </item> |
108 | | - |
109 | 88 | <item> |
110 | 89 | <title>Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling</title> |
111 | 90 | <link>/post/research/klotz2020uncertainty/</link> |
112 | 91 | <pubDate>Wed, 14 Apr 2021 15:23:59 +0530</pubDate> |
113 | | - |
114 | 92 | <guid>/post/research/klotz2020uncertainty/</guid> |
115 | 93 | <description><p>Deep learning based uncertainty estimation techniques and benchmarking procedure for rainfall-runoff modeling.</p></description> |
116 | 94 | </item> |
117 | | - |
118 | 95 | <item> |
119 | 96 | <title>MC-LSTM: Mass-Conserving LSTM</title> |
120 | 97 | <link>/post/research/hoedt2021mclstm/</link> |
121 | 98 | <pubDate>Thu, 14 Jan 2021 08:23:59 +0530</pubDate> |
122 | | - |
123 | 99 | <guid>/post/research/hoedt2021mclstm/</guid> |
124 | 100 | <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> |
125 | 101 | </item> |
126 | | - |
127 | 102 | <item> |
128 | 103 | <title>A Data Scientist's Guide to Streamflow Prediction</title> |
129 | 104 | <link>/post/research/gauch2020guide/</link> |
130 | 105 | <pubDate>Fri, 05 Jun 2020 20:23:59 +0530</pubDate> |
131 | | - |
132 | 106 | <guid>/post/research/gauch2020guide/</guid> |
133 | 107 | <description><p>An introduction to hydrology and especially rainfall-runoff modeling, targeted at data scientists.</p></description> |
134 | 108 | </item> |
135 | | - |
136 | 109 | <item> |
137 | 110 | <title>Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning</title> |
138 | 111 | <link>/post/research/kratzert2019pub/</link> |
139 | 112 | <pubDate>Sat, 23 Nov 2019 20:23:59 +0530</pubDate> |
140 | | - |
141 | 113 | <guid>/post/research/kratzert2019pub/</guid> |
142 | 114 | <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> |
143 | 115 | </item> |
144 | | - |
145 | 116 | <item> |
146 | 117 | <title>The Proper Care and Feeding of CAMELS: How Limited Training Data Affects Streamflow Prediction</title> |
147 | 118 | <link>/post/research/gauch2020feeding/</link> |
148 | 119 | <pubDate>Sun, 17 Nov 2019 20:23:59 +0530</pubDate> |
149 | | - |
150 | 120 | <guid>/post/research/gauch2020feeding/</guid> |
151 | 121 | <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> |
152 | 122 | </item> |
153 | | - |
154 | 123 | <item> |
155 | 124 | <title>Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets</title> |
156 | 125 | <link>/post/research/kratzert2019regional/</link> |
157 | 126 | <pubDate>Fri, 02 Aug 2019 20:23:59 +0530</pubDate> |
158 | | - |
159 | 127 | <guid>/post/research/kratzert2019regional/</guid> |
160 | 128 | <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> |
161 | 129 | </item> |
162 | | - |
163 | 130 | <item> |
164 | 131 | <title>Do internals of neural networks make sense in the context of hydrology?</title> |
165 | 132 | <link>/post/research/kratzert2018agu/</link> |
166 | 133 | <pubDate>Mon, 10 Dec 2018 20:23:59 +0530</pubDate> |
167 | | - |
168 | 134 | <guid>/post/research/kratzert2018agu/</guid> |
169 | 135 | <description><p>Presentation at the AGU 2018 Fall Meeting on experiments regarding the interpretability of LSTM states.</p></description> |
170 | 136 | </item> |
171 | | - |
172 | 137 | </channel> |
173 | 138 | </rss> |
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