Preprints

Filtering by Subject: Artificial Intelligence and Robotics

A Conversational Intelligent Assistant for Enhanced Operational Support in Floodplain Management with Multimodal Data

Vinay Pursnani, Muhammed Yusuf Sermet, Ibrahim Demir

Published: 2024-12-19
Subjects: Artificial Intelligence and Robotics, Civil and Environmental Engineering, Computer Sciences, Databases and Information Systems, Earth Sciences, Environmental Engineering, Environmental Sciences, Environmental Studies, Hydraulic Engineering, Hydrology, Water Resource Management

Floodplain management is crucial for mitigating flood risks and enhancing community resilience, yet floodplain managers often face significant challenges, including the complexity of data analysis, regulatory compliance, and effective communication with diverse stakeholders. This study introduces Floodplain Manager AI, an innovative artificial intelligence (AI) based virtual assistant designed to [...]

Tracking Drought Impacts from Texts: Towards AI-Assisted Drought Impact Detection

Beichen Zhang, Kelly Helm Smith, Frank Schilder, et al.

Published: 2024-12-06
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Environmental Indicators and Impact Assessment, Environmental Sciences, Hydrology

Drought is recognized for its extensive and varied impacts. Based on the drought-related textual datasets from the National Drought Mitigation Center, our research applies advanced artificial intelligence techniques, including deep learning and natural language processing, to enhance the monitoring of multifaceted drought impacts in the United States. This study also delves into predicting [...]

Towards statistical modeling of chlorophyll-a concentrations in Balikpapan Bay, Indonesia: Implications for algal bloom detection

Sandy Hardian Susanto Herho, Iwan Pramesti Anwar, Faruq Khadami, et al.

Published: 2024-10-26
Subjects: Artificial Intelligence and Robotics, Biogeochemistry, Environmental Monitoring, Marine Biology, Oceanography

This study presents a comprehensive statistical analysis of chlorophyll-a dynamics in Balikpapan Bay, Indonesia, combining time series analysis, extreme value modeling, and machine learning techniques to understand phytoplankton variability near Indonesia's planned new capital city. Analysis of daily chlorophyll-a concentrations (2019-2021) revealed a non-Gaussian distribution (skewness = 2.212, [...]

Application of machine learning methods to forecast petrophysical properties in basalts of the Serra Geral Group: Implications for carbon storage

João Paulo Guilherme Rodrigues Alves, Claudio Riccomini

Published: 2024-10-22
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Environmental Sciences, Geophysics and Seismology, Oil, Gas, and Energy, Physical Sciences and Mathematics

This study applies machine learning techniques for forecasting petrophysical properties (density, porosity, and permeability) in the basalts of the Serra Geral Group, located in the Paraná Basin, Brazil. These properties are crucial for the successful implementation of carbon capture and storage (CCS), an important technology to combat climate change. Employing machine learning models—XGBoost, [...]

DARTS: Multi-year database of AI-detected retrogressive thaw slumps in the circum-arctic permafrost region

Ingmar Nitze, Konrad Heidler, Nina Nesterova, et al.

Published: 2024-10-20
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Geomorphology, Physical Sciences and Mathematics

Retrogressive Thaw Slumps (RTS) and Active Layer Detachment Slides (ALD) are widespread thermal mass-wasting hillslope failures triggered by thawing permafrost. Despite increasing rates of these failures, knowledge about their pan-arctic spatial and temporal distribution remains limited. We present the Database of AI-detected Arctic RTS and ALD footprints (DARTS), the largest hillslope [...]

MicroCrystalNet: An Efficient Convolutional Neural Network for Microcrystal Classification using Scanning Electron Microscope Petrography

Mohammed Yaqoob, Mohammed Ishaq, Mohammed Yusuf Ansari, et al.

Published: 2024-08-22
Subjects: Artificial Intelligence and Robotics, Engineering, Other Engineering, Other Environmental Sciences

Morphological characterization of microcrystalline rock textures typically relies upon the visual interpretation and manual measurement of scanning electron microscopy (SEM) imagery: a practice fraught with subjectivity, inefficiency, sampling bias, and data loss. We introduce a state-of-the-art computer vision pipeline, built on deep learning architectures, for segmenting and classifying [...]

Implementation of the Peruvian Earthquake Early Warning System

Pablo Lara, Hernando Tavera, Quentin Bletery, et al.

Published: 2024-06-12
Subjects: Artificial Intelligence and Robotics, Geophysics and Seismology

We present the implementation and testing of the seismological components of the Peruvian Earthquake Early Warning System (Sistema de Alerta Sísmica Peruano, SASPe). SASPe is designed to send alert messages to areas located within a given distance from the epicenter of large (magnitude ≥ 6.0) subduction earthquakes, with a first alert based on data available 3 seconds after the arrival [...]

Integrating Conversational AI Agents for Enhanced Water Quality Analytics: Development of a Novel Data Expert System

Gabriel Michael Vald, Muhammed Yusuf Sermet, Jerry Mount, et al.

Published: 2024-06-01
Subjects: Artificial Intelligence and Robotics, Civil Engineering, Databases and Information Systems, Environmental Engineering, Hydrology, Software Engineering

Despite advancements in environmental monitoring, the gap between data collection and user-friendly data interpretation remains a significant challenge, especially in the domain of water quality management. This paper introduces the Artificial Intelligence Data Expert (AI-DE), a novel data analytics system that is designed to facilitate on-demand analysis of time-series sensor data related to [...]

A Comprehensive Evaluation of Multimodal Large Language Models in Hydrological Applications

Likith Kadiyala, Omer Mermer, Dinesh Jackson Samuel, et al.

Published: 2024-05-25
Subjects: Artificial Intelligence and Robotics, Environmental Monitoring, Hydrology

Large Language Models (LLMs) combined with visual foundation models have demonstrated remarkable advancements, achieving a level of intelligence comparable to human capabilities. In this study, we conduct an analysis of the latest Multimodal LLMs (MLLMs), specifically Multimodal-GPT, GPT-4 Vision, Gemini and LLaVa, focusing on their application in the hydrology domain. The hydrology domain holds [...]

A Review of Machine Learning in Snow Water Equivalent Monitoring

Faye Hsu, Ziheng Sun, Gokul Prathin, et al.

Published: 2024-05-09
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Databases and Information Systems, Earth Sciences, Hydrology, Numerical Analysis and Scientific Computing, Oceanography and Atmospheric Sciences and Meteorology

In recent years, the scientific community focused on snow dynamics has witnessed a surge in efforts aimed at enhancing Snow Water Equivalent (SWE) monitoring capabilities, largely propelled by the incorporation of Machine Learning (ML) techniques. This comprehensive review delves into the current state of research within this evolving domain, shedding light on the indispensable role of precise [...]

Interpretable Quality Control of Sparsely Distributed Environmental Sensor Networks Using Graph Neural Networks

Elżbieta Krystyna Lasota, Timo Houben, Julius Polz, et al.

Published: 2024-05-02
Subjects: Artificial Intelligence and Robotics, Earth Sciences

Environmental sensor networks play a crucial role in monitoring key parameters essential for understanding Earth’s systems. To ensure the reliability and accuracy of collected data, effective quality control (QC) measures are essential. Conventional QC methods struggle to handle the complexity of environmental data. Conversely, advanced techniques such as neural networks, are typically not [...]

Sentinel-1 SAR-based Globally Distributed Landslide Detection by Deep Neural Networks

Lorenzo Nava, Alessandro Cesare Mondini, Kushanav Bhuyan, et al.

Published: 2024-04-05
Subjects: Artificial Intelligence and Robotics, Geomorphology

Efficient response to large and widespread multiple landslide events (MLEs) demands rapid and effective landslide detection. Despite extensive efforts using optical remotely sensed imagery, limitations in global, day & night, and all-weather operational capabilities remain. To address these gaps, we introduce an approach that harnesses Deep Neural Networks (DNNs) and Synthetic Aperture Radar [...]

An ensemble neural network approach for space-time landslide predictive modelling

Jana Lim, Giorgio Santinelli, Ashok Dahal, et al.

Published: 2024-02-27
Subjects: Artificial Intelligence and Robotics, Geomorphology, Multivariate Analysis

There is an urgent need for accurate and effective Landslide Early Warning Systems (LEWS). Most LEWS are currently based on a single temporally-aggregated measure of rainfall derived from either in-situ measurements or satellite-based rainfall estimates. Relying on a summary metric of precipitation may not capture the complexity of the rainfall signal and its dynamics in space and time in [...]

Using computer vision to detect and segment fire behavior classifications in UAS-captured images

Brett Lawrence, Emerson de Lemmus

Published: 2024-02-21
Subjects: Artificial Intelligence and Robotics, Forest Management, Natural Resources and Conservation

The widely adaptable capabilities of artificial intelligence, in particular deep learning and computer vision has led to significant research output regarding fire and smoke detection. Previous studies often focus on themes like early fire detection, increased operational awareness, and post-fire assessment. To further test the capabilities of deep learning detection in these scenarios, we [...]

Deep learning with simulated laser scanning data for 3D point cloud classification

Alberto M. Esmorís, Hannah Weiser, Lukas Winiwarter, et al.

Published: 2024-01-16
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Numerical Analysis and Scientific Computing, Other Earth Sciences

Laser scanning is an active remote sensing technique applied in many disciplines to acquire state-of-the-art spatial measurements. Semantic labeling is often necessary to extract information from the raw point cloud. Deep learning methods constitute a data-hungry solution for the semantic segmentation of point clouds. In this work, we investigate the use of simulated laser scanning for training [...]

search

You can search by:

  • Title
  • Keywords
  • Author Name
  • Author Affiliation