Posters I

Monday, 29 January 2024: 3:00 PM-4:30 PM
Hall E (The Baltimore Convention Center)
Host: 23rd Conference on Artificial Intelligence for Environmental Science

Papers:
130
Extreme Value Analysis of K-Means Single (k=1) Cluster Statistical Distances, a Heuristic Approach with Los Angeles and San Francisco Hourly Temperatures and Monthly Precipitation
Charles J. Fisk, Individual, Newbury Park, CA
Manuscript (1.3 MB)

131
Enhancing Predictability of Southern California Precipitation Using a Multi-Input Multi-Output Autoencoder Network
Hannah Bao, University of Maryland, College Park, MD; and L. S. Passarella, S. Mahajan, and M. J. Molina
Manuscript (1.1 MB)

132
Process-Constrained Bias Correction of NOAA UFS Subseasonal-to-Seasonal (S2S) Precipitation Forecasts through Machine Learning
Kyle Joseph Chen Hall, NOAA, Boulder, CO; CIRES, Boulder, CO; and L. Bengtsson

133
Exploring the Relative and Combined Contribution of the MJO and ENSO to Midlatitude Subseasonal Predictability with an Interpretable Neural Network
William Anthony Manriquez, UCAR, Boulder, CO; and W. E. Chapman and K. J. Mayer

134
Empowering Coastal Resilience: A Multi-Layer Perceptron Approach for Subseasonal-to-Seasonal Sea Level Predictions in the Gulf of Mexico
Marina Vicens Miquel, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; and C. Radin, V. Nieves, P. Tissot, and A. Medrano, PhD

135
An Assessment of the S2S Forecast Skill of a Hybrid Model That Combines Machine Learning with an AGCM.
Dhruvit Patel, University of Maryland, College Park, MD

Handout (961.4 kB)

136
Denoising Raw Photon Counting Atmospheric Lidar Data Using Autoencoders
Patrick A. Selmer, SSAI, Lanham, MD; and J. E. Yorks and E. P. Nowottnick

Handout (2.0 MB)

137
Cloudstream: Long-term Cloud Detection Image Prediction Model Using Multi-Channel Satellite Images
Eunbin Cho, SIA(SI analytics), Daejeon, South korea; SI-Analytics, Daejeon, South korea; and E. Kim and Y. Choi

138
Deep Domain Adaptation Technique for Cloud Property Retrieval with Multiple Satellite Remote Sensor Data
Xin Huang, Towson University, Towson, MD

139
Machine Learning Classification of Single-Particle Mass Spectra
Jose Angel Perez Chavez, Howard University, Washington, DC; and M. A. Zawadowicz

143
Enhancing Air Quality Prediction through Bias Correction using Machine Learning on CMAQ

140
In Search of an Alternative to Ensembles for Estimating Uncertainty in Air Quality Modelling

144
Exploring Relationships Between Environmental Variables and Global Distribution of Particulate Inorganic Carbon versus Particulate Organic Carbon Using Random Forest Analysis
Rui Jin, Johns Hopkins University, Baltimore, MD; and A. Gnanadesikan

141
Generative Deep Learning Models for Air Quality Numeric Model Emulation

145
Predictive Modeling in Environmental Science with Machine Learning Algorithms
Michael Nia, DRI, RENO, NV; and G. Mehdizadeh, PhD student, R. Barjeste Vaezi, E. Erfani, and F. Hosseinpour, PhD

146
Post-Processing NWP Using Deep Learning with a Custom Loss Function to Create Ensemble Forecasts to Quantify Forecast Uncertainty
Ashley Elizabeth Payne, Tomorrow.io, Golden, CO; and L. Conibear, A. Reed Harris, T. McCandless, K. Keshavamurthy, M. E. Green, MA, and S. Flampouris

142
Deep Generative Modeling of Air Quality Tracers

147
Exploring Potential Bias in Data Selection & Processing for Artificial Intelligence in Environmental Sciences
Haley Perez, University of Oklahoma, Norman, OK

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
See more of: 23rd Conference on Artificial Intelligence for Environmental Science
<< Previous Session | Next Session >>