Data and code for "Spatial but not temporal predictability of Korean extreme-rainfall change: limits of covariate machine learning and a climate-factor implementation of the Clausius-Clapeyron / nonstationary-GEV alternative Manuscript TypeResearch Article"

Machine Learning for Predictive Modeling and Simulation of Urban Renewal Using Multi-Source Spatial Data

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Before the Model: Why Datasets and Data Representation Define What Machine Learning Can Learn

Jean Franck Loa Rojas

Machine learning systems do not learn reality directly; they learn from the representations preserved in their datasets. This structured narrative review examines how dataset purpose, coverage, integrity, labeling, independence, reproducibility, governance, and continuity determi…Read more

Stop Spatializing Time: Machine Learning Agents Should Learn Through Time, Not About Time

Teeratham Vitchutripop, Alyssa Quarles, Wei Zhang, Daniel Rakita

Modern machine learning systems are increasingly deployed in settings that require persistent interaction, adaptation, memory, and decision-making over time. Yet, most learning paradigms remove the temporal pressures faced by physically embedded agents: the world waits for comput…Read more

Code and data for: Leakage-audited machine learning versus ETAS for earthquake forecasting in the Sea of Marmara

Basri Kerem Alhan, Kenessary Khabat

Code, processed data products, configuration, and results artifacts for "Machine learning versus ETAS for earthquake forecasting in the Sea of Marmara: a leakage-audited negative result and a closed-form scoring artifact" (Alhan & Khabat, submitted to Seismica). Version 1.2.0 acc…Read more

Predictive Climate Finance: Spatiotemporal Machine Learning and Cloud-Native Middleware for Modeling Agricultural Financial Anomalies

YINKA ADERIBIGBE

The integration of climate finance and empirical asset pricing is frequently constrained by the latency between environmental anomalies and financial market reactions. Traditional econometric models evaluating biodiversity exposure and agricultural commodity pricing rely heavily…Read more

R Code for: Bayesian Additive Regression Trees for Circular Data: A Machine Learning Framework

Talal Kurdi, Saralees Nadarajah

This repository contains the R code used in the paper "Bayesian Additive Regression Trees for Circular Data: A Machine Learning Framework" by Talal Kurdi and Saralees Nadarajah. The code implements Bayesian Additive Regression Trees (BART) methods for regression with circular dat…Read more