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<title>IDE</title>
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<div class="container">
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<h1 id="logo"><a><img src="IDE/assets/images/neurips-navbar-logo.svg" width="80" height="80"/></a><a href="IDE.html"> Identifying Spatio-Temporal Drivers of Extreme Events</a></h1>
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<p>
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<a href="https://hakamshams.github.io/">Mohamad Hakam Shams Eddin</a><sup>1,2</sup> and
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<a href="http://pages.iai.uni-bonn.de/gall_juergen/">Juergen Gall</a><sup>1,2</sup>
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<br>
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<br>
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<sup>1</sup>Institute of Computer Science, University of Bonn, Germany
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<br>
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<sup>2</sup>Lamarr Institute for Machine Learning and Artificial Intelligence, Germany
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</p>
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<nav id="nav">
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<ul>
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<li><a class="icon1 ai ai-open-access" href="https://openreview.net/forum?id=DdKdr4kqxh"><span>Paper</span></a></li>
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<li><a class="icon1 ai ai-arxiv" href="https://arxiv.org/abs/2410.24075"><span>ArXiv</span></a></li>
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<li><a class="icon brands alt fa-youtube" href="https://www.youtube.com/watch?v=_AD5moplxB0"><span>Video</span></a></li>
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<li><a class="icon brands alt fa-github" href="https://github.com/HakamShams/IDEE"><span>Code</span></a></li>
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<li><a class="icon1 ai ai-open-data" href="https://doi.org/10.60507/FK2/RD9E33"><span>Dataset</span></a></li>
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</ul>
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</nav>
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</section>
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<section style="background: #f0f0f0; padding: 0em 0 0em 0">
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<div class="container" >
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<div class="video main">
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<iframe src="https://www.youtube.com/embed/_AD5moplxB0" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
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</div>
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</section>
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<section id="main">
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<div class="container">
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<div id="abstract content">
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<article class="box post">
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<header>
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<h2>Abstract</h2>
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<p align="justify" style="color:black"> The spatio-temporal relations of extreme events impacts and their drivers in climate data are not fully understood and there is a need of machine learning approaches to identify such spatio-temporal relations from data.
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The task, however, is very challenging since there are time delays between extremes and their drivers, and the spatial response of such drivers is inhomogeneous.
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In this work, we propose a first approach and benchmarks to tackle this challenge.
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Our approach is trained end-to-end to predict spatio-temporally extremes and spatio-temporally drivers in the physical input variables jointly.
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We assume that there exist precursor drivers, primarily as anomalies in assimilated land surface and atmospheric data, for every observable impact of extremes.
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By enforcing the network to predict extremes from spatio-temporal binary masks of identified drivers, the network successfully identifies drivers that are correlated with extremes.
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We evaluate our approach on three newly created synthetic benchmarks where two of them are based on remote sensing or reanalysis climate data and on two real-world reanalysis datasets. </p>
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</header>
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</article>
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</div>
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</section>
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<section style="background: #000000; padding: 1em 0 4em 0">
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<div class="container">
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<div class="row aln-center">
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<div class="col-9">
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<article class="box post">
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<header>
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<h2 style="color:#FFFFFF; text-align: center">Overeview</h2>
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</header>
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<a href="IDE/assets/images/overview.jpg" class="image featured"><img src="IDE/assets/images/overview.jpg" alt="Overview of the task" /></a>
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<p style="color:#FFFFFF;">Overview of the objective of this work.
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We are interested in identifying spatio-temporal relations between the measurable <strong><span style="color:#BC82B5"> impacts of extreme events </span></strong> like the vegetation health index and their <strong><span style="color:#ff4b4b"> drivers</span></strong>.
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As drivers, we focus on anomalies in state variables of the land-atmosphere and hydrological cycle.
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The task is very challenging since the drivers can occur at a different region than the extreme event and earlier in time.
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</p>
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</article>
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</div>
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</div>
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</div>
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</section>
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<section style="background: #ffffff; padding: 0em 0 2em 0">
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<div class="container">
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<div class="row aln-center">
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<div class="col-9">
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<article class="box post">
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<header>
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<h2>Model architecture</h2>
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</header>
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<a href="IDE/assets/images/model.jpg" class="image featured"><img src="IDE/assets/images/model.jpg" alt="model design" /></a>
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<p>An overview of the proposed model to identify the spatio-temporal relations between extreme agricultural droughts and drivers.
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The input variables are first encoded into features.
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In a subsequent step, a lockup free quantization layer (LFQ) takes the extracted features and classifies the variables into a binary representation of normal or anomalous events.
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Finally, a classifier is used to predict extreme events from the identified anomalies.</p>
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</article>
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</div>
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</div>
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</div>
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</section>
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<section id="features" style="background: #FFFFFF; padding: 0em 0 2em 0">
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<div class="container">
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<header>
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<h2>Comparison to the baselines</h2>
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</header>
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<div class="row aln-center">
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<div class="col-5">
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<section>
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<a href="IDE/assets/images/table.png" class="image featured"><img src="IDE/assets/images/table.png" alt="Comparison to the baselines 1" /></a>
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</section>
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</div>
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<div class="col-5">
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<section>
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<a href="IDE/assets/images/dropped_variabels.png" class="image featured"><img src="IDE/assets/images/dropped_variabels.png" alt="Comparison to the baselines 2" /></a>
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</section>
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</div>
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</div>
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</div>
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<section style="background: #000000; padding: 1em 0 2em 0">
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<div class="container">
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<header><h2 style="color:#FFFFFF; text-align: center">Results on real-world reanalysis data</h2></header>
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<div class="row aln-center">
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<div class="col-10">
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<div class="carousel results-carousel">
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<div class="item">
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<img class="image fit" src="IDE/assets/images/AFR_2017020.jpg" alt="Africa (AFR-11)"/>
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<h3 class="header" style="color: white; text-align: center">
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Qualitative results on <span style="color:#cd932b"> ERA5-Land </span> for <span style="color:#cd932b"> Africa (AFR-11)</span>.
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Shown are the identified drivers and anomalies for each variable along with the prediction of extreme agricultural droughts on the top left.
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</h3>
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</div>
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<div class="item">
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<img class="image fit" src="IDE/assets/images/NAM_2018019.jpg" alt="North America (NAM-11)"/>
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<h3 class="header" style="color: white; text-align: center">
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Qualitative results on <span style="color:#cd932b"> ERA5-Land </span> for <span style="color:#cd932b"> North America (NAM-11)</span>.
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Shown are the identified drivers and anomalies for each variable along with the prediction of extreme agricultural droughts on the top left. </h3>
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</div>
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<div class="item">
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<img class="image fit" src="IDE/assets/images/SAM_2019050.jpg" alt="South America (SAM-11)"/>
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<h3 class="header" style="color: white; text-align: center">
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Qualitative results on <span style="color:#cd932b"> ERA5-Land </span> for <span style="color:#cd932b"> South America (SAM-11)</span>.
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Shown are the identified drivers and anomalies for each variable along with the prediction of extreme agricultural droughts on the top left. </h3>
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</div>
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<div class="item">
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<img class="image fit" src="IDE/assets/images/EAS_2020009.jpg" alt="East Asia (EAS-11)"/>
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<h3 class="header" style="color: white; text-align: center">
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Qualitative results on <span style="color:#cd932b"> ERA5-Land </span> for <span style="color:#cd932b"> East Asia (EAS-11)</span>.
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Shown are the identified drivers and anomalies for each variable along with the prediction of extreme agricultural droughts on the top left. </h3>
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</div>
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<div class="item">
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<img class="image fit" src="IDE/assets/images/CAS_2021026.jpg" alt="Central Asis (CAS-11)"/>
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<h3 class="header" style="color: white; text-align: center">
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Qualitative results on <span style="color:#cd932b"> ERA5-Land </span> for <span style="color:#cd932b"> Central Asis (CAS-11)</span>.
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Shown are the identified drivers and anomalies for each variable along with the prediction of extreme agricultural droughts on the top left. </h3>
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</div>
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<div class="item">
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<img class="image fit" src="IDE/assets/images/EUR_2022046.jpg" alt="Europe"/>
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<h3 class="header" style="color: white; text-align: center">
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Qualitative results on <span style="color:#cd932b"> CERRA </span> for <span style="color:#cd932b"> Europe </span>.
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Shown are the identified drivers and anomalies for each variable along with the prediction of extreme agricultural droughts on the top left. </h3>
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</div>
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<div class="item">
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<img class="image fit" src="IDE/assets/images/CERRA_2020045.jpg" alt="Europe (EUR-11)"/>
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<h3 class="header" style="color: white; text-align: center">
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Qualitative results on <span style="color:#cd932b"> ERA5-Land </span> for <span style="color:#cd932b"> Europe (EUR-11)</span>.
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Shown are the identified drivers and anomalies for each variable along with the prediction of extreme agricultural droughts on the top left. </h3>
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</div>
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</div>
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</div>
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</div>
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</div>
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</section>
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<section style="background: #FFFFFF; padding: 1em 0 0em 0">
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<div class="container">
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<header><h2 style="color:#000000; text-align: center">Results on real-world reanalysis data</h2></header>
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<div class="row aln-center" >
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<div class="col-11">
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<article class="box post">
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<a href="IDE/assets/gifs/qualitative_NAM.gif" class="image fit"><img src="IDE/assets/gifs/qualitative_NAM.gif" alt="Qualitative results on the ERA5-Land NAM-11 reanalysis" /></a>
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<p>Qualitative results on <strong><span style="color:#cd932b"> ERA5-Land </span></strong> for <strong><span style="color:#cd932b"> North America (NAM-11)</span></strong>.
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Shown are the identified drivers and anomalies for two variables along with the prediction of extreme agricultural droughts on the top right.</p>
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</article>
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</div>
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</div>
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</div>
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</section>
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<section style="background: #FFFFFF; padding: 1em 0 3em 0">
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<div class="container">
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<header><h2 style="color:#000000; text-align: center">Results on synthetic data</h2></header>
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<div class="row aln-center" >
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<div class="col-11">
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<article class="box post">
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<a href="IDE/assets/gifs/baselines.gif" class="image fit"><img src="IDE/assets/gifs/baselines.gif" alt="Qualitative results on the synthetic CERRA reanalysis" /></a>
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<p>Qualitative results on the synthetic CERRA reanalysis from the test set.
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Shown are the <strong><span style="color:#008080">prediction</span></strong>, the <strong><span style="color:#000080">ground truth</span></strong>, and the <strong><span style="color:#ff0000">false positive</span></strong>.
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Albedo and relative humidity are not correlated with extremes, meaning that they do not have target anomalies but only random ones.</p>
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</article>
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</div>
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</div>
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</div>
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</section>
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<section style="background: #000000; padding: 2em 0 2em 0">
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<div class="container">
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<header><h2 style="color:#FFFFFF; text-align: center">Results on real-world reanalysis data </h2></header>
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<div class="row aln-center">
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<div class="col-6">
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<section>
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<a href="IDE/assets/images/spatial_portugal.jpg" class="image featured"><img src="IDE/assets/images/spatial_portugal.jpg" alt="The averaged spatial distribution of anomalies" /></a>
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<p style="color:#FFFFFF;">The averaged spatial distribution of drivers/anomalies related to <span style="color:#f6c68e">Portugal in Europe</span>.
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For this experiment, we use prediction on EUR-11 from ERA5-Land and select frames (weeks) within the period 2018-2024 where there were extreme drought of at least 25% of the pixels in Portugal.
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Then we normalize the identified anomalies by the total number of frames to obtain the final map.
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</p>
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</section>
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</div>
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<div class="col-6">
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<section>
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<a href="IDE/assets/images/spatial_nordrhein_westfalen.jpg" class="image featured"><img src="IDE/assets/images/spatial_nordrhein_westfalen.jpg" alt="The averaged spatial distribution of anomalies" /></a>
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<p style="color:#FFFFFF;">The averaged spatial distribution of anomalies related to a specific place in <span style="color:#f6c68e"> Europe (North Rhine-Westphalia)</span>.
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For this experiment, we use prediction on EUR-11 from ERA5-Land and select frames (weeks) within the period 2018-2024 where there were extreme drought of at least 25% of the pixels in the North Rhine-Westphalia.
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Then we normalize the identified anomalies by the total number of frames to obtain the final map.
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</p>
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</section>
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</div>
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</div>
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</div>
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</section>
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<section id="features" style="background: #FFFFFF; padding: 1em 0 2em 0">
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<div class="container">
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<header>
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<h2>Real-world reanalysis dataset</h2>
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</header>
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<div class="row aln-center">
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<div class="col-6">
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<section>
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<header>
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<h3> The definition of the domains used in the study </h3>
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</header>
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<a href="IDE/assets/gifs/domain_1.gif" class="image featured"><img src="IDE/assets/gifs/domain_1.gif" alt="The definition of the domains" /></a>
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<p>
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We conducted the experiments on two real-world reanalysis (ERA5-Land and CERRA) including data from five continents.
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ERA5-Land reanalysis is mapped onto the CORDEX domains.
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CERRA has its own domain definition.
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</p>
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</section>
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</div>
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<div class="col-6">
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<section>
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<header>
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<h3>CORDEX Domains</h3>
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</header>
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<a href="IDE/assets/gifs/domain_2.gif" class="image featured"><img src="IDE/assets/gifs/domain_2.gif" alt="CORDEX Domains" /></a>
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</section>
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</div>
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</div>
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</div>
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</section>
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<section id="model" style="background: #ffffff; padding: 0em 0 2em 0">
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<div class="container">
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<div class="row aln-center">
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<div class="col-10">
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<article class="box post">
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<header>
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<h2>Synthetic dataset</h2>
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</header>
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<a href="IDE/assets/gifs/t_less.gif" class="image featured"><img src="IDE/assets/gifs/t_less.gif" alt="Perceptual examples of the synthetic CERRA reanalysis data" /></a>
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<p>
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Perceptual examples of the <strong> synthetic CERRA reanalysis data</strong>.
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The target anomalies are visualized under each variable directly.
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Here, albedo and relative humidity are not correlated with the extremes.
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</p>
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</article>
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</div>
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</div>
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</div>
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</section>
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<section id="features" style="background: #FFFFFF; padding: 1em 0 2em 0">
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<div class="container">
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<header>
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<h3>Perceptual examples of the synthetic data</h3>
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</header>
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<div class="row aln-center">
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<div class="col-6">
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<section>
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<header>
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<h3>Synthetic artificial data </h3>
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</header>
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<a href="IDE/assets/images/1314_14_exp1.jpg" class="image featured"><img src="IDE/assets/images/1314_14_exp1.jpg" alt="Synthetic artificial data" /></a>
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<p> The target anomalies are visualized under each variable directly.
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Here, variables 01 and 05 are not correlated with the extremes.</p>
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</section>
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</div>
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<div class="col-6">
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<section>
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<header>
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<h3>Synthetic CERRA reanalysis data </h3>
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</header>
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<a href="IDE/assets/images/1314_14_exp_3.jpg" class="image featured"><img src="IDE/assets/images/1314_14_exp_3.jpg" alt="Synthetic CERRA reanalysis data" /></a>
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<p> The target anomalies are visualized under each variable directly.
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Here, albedo and relative humidity are not correlated with the extremes.</p>
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</section>
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</div>
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</div>
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</div>
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</section>
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<section style="background: #000000; padding: 2em 0 2em 0">
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<div class="container">
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<header><h2 style="color:#FFFFFF; text-align: center">Visualization of the generated signals Φ</h2></header>
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<div class="row aln-center">
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<div id="content" class="col-12">
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<div id="results-carousel" class="carousel results-carousel">
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<div class="item">
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<img class="image fit" src="IDE/assets/images/time_series_75_75_0.jpg" alt="albedo"/>
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<h3 class="header" style="color: white; text-align: center">
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Synthetic signal of <span style="color:#f6c68e">albedo</span> from CERRA climatology.
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</h3>
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</div>
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<div class="item">
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<img class="image fit" src="IDE/assets/images/time_series_75_75_1.jpg" alt="2m temperature"/>
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<h3 class="header" style="color: white; text-align: center">
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Synthetic signal of <span style="color:#d8af80">2m temperature</span> from CERRA climatology.
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</h3>
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</div>
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<div class="item">
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<img class="image fit" src="IDE/assets/images/time_series_75_75_2.jpg" alt="total cloud cover"/>
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<h3 class="header" style="color: white; text-align: center">
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Synthetic signal of <span style="color:#d8af80">total cloud cover</span> from CERRA climatology.
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</h3>
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</div>
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<div class="item">
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<img class="image fit" src="IDE/assets/images/time_series_75_75_3.jpg" alt="total precipitation"/>
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<h3 class="header" style="color: white; text-align: center">
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Synthetic signal of <span style="color:#d8af80">total precipitation</span> from CERRA climatology.
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</h3>
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</div>
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<div class="item">
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<img class="image fit" src="IDE/assets/images/time_series_75_75_4.jpg" alt="relative humidity"/>
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<h3 class="header" style="color: white; text-align: center">
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Synthetic signal of <span style="color:#d8af80">relative humidity</span> from CERRA climatology.
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</h3>
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</div>
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<div class="item">
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<img class="image fit" src="IDE/assets/images/time_series_75_75_5.jpg" alt="volumetric soil moisture"/>
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<h3 class="header" style="color: white; text-align: center">
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Synthetic signal of <span style="color:#d8af80">volumetric soil moisture</span> from CERRA climatology.
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</h3>
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</div>
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</div>
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</div>
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</div>
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</div>
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</section>
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<section style="background: white; padding: 2em 0 0em 0">
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<div class="container">
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<div class="row aln-center">
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<div class="col-11">
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<article class="box post">
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<header>
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<h2 align="left">Poster</h2>
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</header>
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<a href="IDE/assets/images/Shams_Gall_reduziert.jpg" class="image featured"><img src="IDE/assets/images/Shams_Gall_reduziert.jpg" alt="Poster" /></a>
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</article>
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</div>
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</div>
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</div>
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</section>
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<section class="section" id="BibTeX" style="background: #F2F2F2; padding: 1em 0 1em 0">
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<div class="container is-max-desktop content aln-center">
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<h2 class="title">BibTeX</h2>
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<pre style="text-align: left">
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@inproceedings{IDEE,
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author = {Shams Eddin, Mohamad Hakam and Gall, J\"{u}rgen},
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booktitle = {Advances in Neural Information Processing Systems},
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editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
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pages = {93714--93766},
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publisher = {Curran Associates, Inc.},
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title = {Identifying Spatio-Temporal Drivers of Extreme Events},
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url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/aa7259c82d642e47d5661f3218cdcad2-Paper-Conference.pdf},
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|
volume = {37},
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|
year = {202d4}
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}
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</pre>
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</div>
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</section>
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<section style="background: #ffffff; padding: 2em 0 3em 0">
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<div class="container">
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<header><h2 style="color:#000000; text-align: left">Dataset download </h2></header>
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<div class="row aln-center" >
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<script src="https://bonndata.uni-bonn.de/resources/js/widgets.js?persistentId=doi:10.60507/FK2/RD9E33&dvUrl=https://bonndata.uni-bonn.de&widget=iframe&heightPx=500"></script>
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</div>
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</div>
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<div class="columns is-centered">
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<div class="content">
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<p align="center">
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This website is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/" target="_blank">Creative
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Commons Attribution-ShareAlike 4.0 International License</a>. You are free to use the <a href="https://github.com/HakamShams/IDE">source code</a> of this website, we only ask you to give appropriate credits by mentioning the <a href="IDE.html">IDE</a> website and to consider the licences of the source codes.
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</p>
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<h3 align="center" style="color: white; font-size:22px"> Contact: <a href="mailto:shams@iai.uni-bonn.de">shams@iai.uni-bonn.de</a>
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<a href="mailto:gall@iai.uni-bonn.de">gall@iai.uni-bonn.de</a>
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</h3>
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</div>
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</div>
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<div id="copyright" class="container">
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<ul class="links">
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<li>© All rights reserved</li>
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<li>Design: <a href="https://hakamshams.github.io/Focal-TSMP/">Focal-TSMP</a> & <a href="http://html5up.net">HTML5 UP</a> &
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<a href="https://nerfies.github.io">Nerfie</a>
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