Eighth Workshop on Data Mining in Earth System Science (DMESS 2018)
Co-conveners: Forrest M. Hoffman, Jitendra Kumar, Sarat Sreepathi, and Soo Kyung Kim
In conjunction with the IEEE International Conference on Data Mining (ICDM 2018)
Singapore — November 17, 2018
Spanning many orders of magnitude in time and space scales, Earth science data, from point measurements to process-based Earth system model output, are increasingly large and complex, and often represent very long time series, making these data difficult to analyze, visualize, interpret, and understand. An “explosion” of heterogeneous, multi-disciplinary data—including observations and models of interacting natural, engineered, and human systems—have rendered traditional means of integration and analysis ineffective, necessitating the application of new analytical methods and the development of highly scalable software tools for synthesis, assimilation, comparison, and visualization. For complex, nonlinear feedbacks among chaotic processes, new methods and approaches for data mining and computational statistics are required for classification and change detection, model evaluation and benchmarking, uncertainty quantification, and incorporation of constraints from physics, chemistry, and biology into analysis. This workshop explores various data mining approaches and algorithms for understanding nonlinear dynamics of weather and climate systems and their interactions with biogeochemical cycles, impacts of natural system responses and climate extremes on engineered systems and interdependent infrastructure networks, and mitigation and adaptation strategies for natural hazards and infrastructure and ecosystem resilience. Encouraged are original research papers describing applications of statistical and data mining methods that support analysis and discovery in climate predictability, attributions, weather extremes, water resources management, risk analysis and hazards assessment, ecosystem sustainability, infrastructure resilience, and geo-engineering.
Rigorous review papers that either have the potential to expose data mining researchers to commonly used data-driven methods in the Earth sciences or discuss the applicability and caveats of such methods from a machine learning or statistical perspective, are also desired. Methods may include, but are not limited to cluster analysis, empirical orthogonal functions (EOFs), extreme value and rare events analysis, genetic algorithms, neural networks and deep learning methods, physics-constrained data analytics, automated data assimilation, and other machine learning techniques. Novel approaches that bring new ideas from nonlinear dynamics and information theory, network science and graphical methods, and the state-of-the-art in computational statistics and econometrics, into data mining and machine learning, are particularly encouraged.
- Seventh Workshop on Data Mining in Earth System Science (DMESS 2017)
- Sixth Workshop on Data Mining in Earth System Science (DMESS 2015)
- Fifth Workshop on Data Mining in Earth System Science (DMESS 2014)
- Fourth Workshop on Data Mining in Earth System Science (DMESS 2013)
- Third Workshop on Data Mining in Earth System Science (DMESS 2012)
- Second Workshop on Data Mining in Earth System Science (DMESS 2011)
Convention Center at Resort World Sentosa (RWS) at Sentosa Island, Singapore
November 17, 2018
Gemini 1 Room - Co-chairs: Jitendra Kumar and Soo Kyung Kim
|Time||Paper ID||Presenter||Title||Authors||Contact Email|
|13:30||Sookyung Kim||Deep-Hurricane-Tracker: Tracking and Forecasting Extreme Climate Events||Sookyung Kim, Hyojin Kim, Joonseok Lee, Sangwoong Yoon, Samira E. Kahou, Mr Prabhat, and Karthik Kashinathemail@example.com|
|14:00||S24205||Antonio Liotta||A Residual Convolution Neural Network for Sea Ice Classification with Sentinel-1 SAR Imagery 10.1109/ICDMW.2018.00119||Wei Song, Minghui Li, Qi He, Dongmei Huang, Cristian Perra, and Antonio Liottafirstname.lastname@example.org|
|14:30||S24203||Jitendra Kumar||Parallel k-means Clustering of Geospatial Data Sets Using Manycore CPU Architectures 10.1109/ICDMW.2018.00118||Richard Mills, Vamsi Sripathi, Jitendra Kumar, Sarat Sreepathi, Forrest Hoffman, and William Hargroveemail@example.com|
|15:30||S24206||Edward Collier||Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images 10.1109/ICDMW.2018.00115||Edward Collier, Kate Duffy, Sangram Ganguly, Geri Madanguit, Subodh Kalia, Gayaka Shreekant, Ramakrishna Nemani, Andrew Michaelis, Shuang Li, Auroop Ganguly, and Supratik Mukhopadhyayfirstname.lastname@example.org|
|16:00||S24208||Zachary Langford||Wildfire Mapping in Interior Alaska Using Deep Neural Networks on Imbalanced Datasets 10.1109/ICDMW.2018.00116||Zachary Langford, Jitendra Kumar, and Forrest Hoffmanemail@example.com|
|16:30||S24207||Udit Bhatia||Extreme Values from Spatiotemporal Chaos: Precipitation Extremes and Climate Variability 10.1109/ICDMW.2018.00114||Udit Bhatia and Auroop Ratan Gangulyfirstname.lastname@example.org|
|17:00||S24204||Jitendra Kumar||Detecting outliers in streaming time series data from ARM distributed sensors 10.1109/ICDMW.2018.00117||Yuping Lu, Jitendra Kumar, Nathan Collier, Bhargavi Krishna, and Michael Langstonemail@example.com|
|17:30||S24202||Donglin Zhan||Small-Scale Demographic Sequences Projection Based on Time Series Clustering and LSTM-RNN 10.1109/ICDMW.2018.00120||Donglin Zhan, Shiyu Yi, and Denglin Jiangfirstname.lastname@example.org|
Forrest M. Hoffman is a Senior Computational Climate Scientist at Oak Ridge National Laboratory (ORNL). As a resident researcher in ORNL’s Climate Change Science Institute (CCSI) and a member of ORNL’s Computational Sciences & Engineering Division (CSED), Forrest develops and applies Earth system models (ESMs) to investigate the global carbon cycle and feedbacks between biogeochemical cycles and the climate system. He applies data mining methods using high performance computing to problems in landscape ecology, remote sensing, and large-scale climate data analytics. He founded the workshop series on Data Mining in Earth System Science (DMESS) in 2009 and has served as lead convener for all six prior workshops. Forrest is also a Joint Faculty Professor in the University of Tennessee’s Department of Civil & Environmental Engineering in nearby Knoxville, Tennessee.
Jitendra Kumar is a computational hydrologist at Oak Ridge National Laboratory and a Joint Assistant Professor at the University of Tennessee, Knoxville. He conducts research at the intersection of high performance computing, environmental and Earth sciences, and systems analysis and data mining. His research entails data mining, large-scale global optimization, computational hydrology and hydrogeology, landscape ecology, remote sensing, and development of parallel algorithms for large-scale supercomputers.
Sarat Sreepathi is a Computer Scientist in the Future Technologies Group at Oak Ridge National Laboratory. He is working on development of the E3SM-Multiscale Modeling Framework (MMF) as part of the Exascale Computing Project (ECP). Additionally, he contributes to E3SM as a member of the core performance group. His research interests include High Performance Computing, Performance Analytics, Exascale Co-design, Optimization Algorithms, Computational Intelligence, Parallel I/O, Performance Analysis and Optimization.
Sookyung Kim is a postdoctoral researcher at Lawrence Livermore National Laboratory, pursuing research in deep learning for climate data analysis. She works on design and development of convolutional neural networks (CNNs) to detect and localize hurricanes in massive climate model output and development of models to track time sequenced tropical cyclones from historical climate data.
- Michael W. Berry (Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee, USA)
- Bjørn-Gustaf J. Brooks (Eastern Forest Environmental Threat Assessment Center, USDA Forest Service, Asheville, North Carolina, USA)
- Nathan Collier (Computational Earth Sciences Group, Computational Sciences & Engineering Division and Oak Ridge Climate Change Science Institute (CCSI), Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA)
- Auroop R. Ganguly (Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts, USA)
- Sangram Ganguly (Bay Area Environmental Research Institute and NASA Ames Research Center, California, USA)
- William W. Hargrove (Eastern Forest Environmental Threat Assessment Center, USDA Forest Service, Asheville, North Carolina, USA)
- Forrest M. Hoffman (Computational Earth Sciences Group, Computational Sciences & Engineering Division and Oak Ridge Climate Change Science Institute (CCSI), Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA)
- Jian Huang (Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee USA)
- Soo Kyung Kim (Lawrence Livermore National Laboratory, Livermore, California, USA)
- Evan Kodra (risQ Incorporated, Cambridge, Massachusetts, USA)
- Jitendra Kumar (Terrestrial Systems Modeling Group, Environmental Sciences Division and Oak Ridge Climate Change Science Institute (CCSI), Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA)
- Vipin Kumar (Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA)
- Zachary L. Langford (Boeing Research & Technology, Huntsville, Alabama, USA)
- Miguel D. Mahecha (Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, GERMANY)
- Richard T. Mills (Laboratory for Advanced Numerical Simulations, Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois, USA)
- Steven P. Norman (Eastern Forest Environmental Threat Assessment Center, USDA Forest Service, Asheville, North Carolina, USA)
- Sarat Sreepathi (Computer Science & Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA)
- Vamsi Sripathi (Intel Corporation, Hillsboro, Oregon, USA)
- Karsten Steinhaeuser (Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA)
- Min Xu (Computational Earth Sciences Group, Computational Sciences & Engineering Division and Oak Ridge Climate Change Science Institute (CCSI), Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA)
- Sander Veraverbeke (Vrije Universiteit Amsterdam, Amsterdam, NETHERLANDS)
- Yawei Hui (Computer Science & Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA)
Authors are invited to submit manuscripts of 8 pages (10 pages maximum for additional fee) reporting unpublished, mature, and original research and recent developments/theoretical considerations in applications of data mining to Earth sciences by August 7, 2017, in IEEE 2-column format. Accepted papers will be printed in the conference proceedings. Submission implies the willingness of at least one of the authors to register and present the paper.
Please submit your paper via this website
For manuscripts that have been accepted to the workshop, please submit camera-ready papers via this website and complete the copyright forms and upload papers for publication via IEEE CPS website by September 28, 2018.
- Full Paper submission :
August 7,August 21, 2018
- Author notification : September 4, 2018
- Camera-ready deadline :
September 15,September 28, 2018
- Copyright forms due :
September 15,September 28, 2018
- DMESS 2018 Workshop : November 17, 2018
- ICDM 2018 Conference : November 17–20, 2018
Email: dmess2018 at climatemodeling dot org