36 citations · 49 across the 20 of their papers we have counts for
14 papers · 1 filter
Accurate, Efficient, and Explainable Deep Learning Approaches for Environmental Science Problems
Jimeng Shi
Environmental science plays a pivotal role in safeguarding ecosystems, a domain driven by large-scale, heterogeneous data. In the big data era, artificial intelligence (AI) has eme…
Retrieval-Augmented Foundation Models for Water Level Prediction in the Everglades
Rahuul Rangaraj, Jimeng Shi, Rajendra Paudel +2
Accurate water level forecasting in the Everglades is essential for flood mitigation, drought management, water resource planning, and biodiversity conservation. While recent time-…
Hypercube-Based Retrieval-Augmented Generation for Scientific Question-Answering
Jimeng Shi, Sizhe Zhou, Bowen Jin +5
Large language models (LLMs) often need to incorporate external knowledge to solve theme-specific problems. Retrieval-augmented generation (RAG) has shown its high promise, empower…
How Effective are Large Time Series Models in Hydrology? A Study on Water Level Forecasting in Everglades
Rahuul Rangaraj, Jimeng Shi, Azam Shirali +3
The Everglades play a crucial role in flood and drought regulation, water resource planning, and ecosystem management in the surrounding regions. However, traditional physics-based…
Deep Learning and Foundation Models for Weather Prediction: A Survey
Jimeng Shi, Azam Shirali, Bowen Jin +10
Physics-based numerical models have been the bedrock of atmospheric sciences for decades, offering robust solutions but often at the cost of significant computational resources. De…
ReFine: Boosting Time Series Prediction of Extreme Events by Reweighting and Fine-tuning
Jimeng Shi, Azam Shirali, Giri Narasimhan
Extreme events are of great importance since they often represent impactive occurrences. For instance, in terms of climate and weather, extreme events might be major storms, floods…
CoDiCast: Conditional Diffusion Model for Global Weather Prediction with Uncertainty Quantification
Jimeng Shi, Bowen Jin, Jiawei Han +2
Accurate weather forecasting is critical for science and society. Yet, existing methods have not managed to simultaneously have the properties of high accuracy, low uncertainty, an…
TimeX++: Learning Time-Series Explanations with Information Bottleneck
Zichuan Liu, Tianchun Wang, Jimeng Shi +7
Explaining deep learning models operating on time series data is crucial in various applications of interest which require interpretable and transparent insights from time series s…
FIDLAR: Forecast-Informed Deep Learning Architecture for Flood Mitigation
Jimeng Shi, Zeda Yin, Arturo Leon +2
In coastal river systems, frequent floods, often occurring during major storms or king tides, pose a severe threat to lives and property. However, these floods can be mitigated or…
The Power of Explainability in Forecast-Informed Deep Learning Models for Flood Mitigation
Jimeng Shi, Vitalii Stebliankin, Giri Narasimhan
Floods can cause horrific harm to life and property. However, they can be mitigated or even avoided by the effective use of hydraulic structures such as dams, gates, and pumps. By…
Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates
Jimeng Shi, Vitalii Stebliankin, Zhaonan Wang +2
Floods can be very destructive causing heavy damage to life, property, and livelihoods. Global climate change and the consequent sea-level rise have increased the occurrence of ext…
Deep Learning Models for Flood Predictions in South Florida
Jimeng Shi, Zeda Yin, Rukmangadh Myana +5
Simulating and predicting the water level/stage in river systems is essential for flood warnings, hydraulic operations, and flood mitigations. Physics-based detailed hydrological a…
Explainable Parallel RCNN with Novel Feature Representation for Time Series Forecasting
Jimeng Shi, Rukmangadh Myana, Vitalii Stebliankin +2
Accurate time series forecasting is a fundamental challenge in data science. It is often affected by external covariates such as weather or human intervention, which in many applic…
Time Series Forecasting (TSF) Using Various Deep Learning Models
Jimeng Shi, Mahek Jain, Giri Narasimhan
Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learnin…