arXiv:2608. 10233v1 Announce Type: cross Abstract: Groundwater variability in Ghana remains poorly characterized due to limited long-term in-situ observations.
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arXiv:2509.18123v2 Announce Type: replace Abstract: Accurate interpretation of soil moisture patterns is critical for irrigation scheduling and crop management, yet existing approaches for soil moist...
RIFT (Relative Isolation From Trees) is a deterministic anomaly detection method that builds a minimum spanning tree and scores points by the sum of apparent edge sizes from each point. For one-dimensional data, RIFT reproduces the closed-form limit of Isolation Forest exactly, while in higher dimensions it offers a parameter‑free, robust alternative that avoids axis‑parallel artifacts and handles varying density and clustered anomalies. An ensemble variant is proposed for large datasets, and experiments on synthetic data and the ADBench benchmark show comparable accuracy to Isolation Forest with reduced variance across random seeds.
arXiv:2607. 12454v1 Announce Type: new Abstract: Multivariate Time Series Anomaly Detection (MTSAD) is essential for reliability and safety in domains such as industrial process monitoring and financial risk management, yet conventional approaches rely on application-specific models that are costly to train and hard to scale.
The study presents a Teacher-Student machine‑learning framework that reconstructs high‑resolution groundwater depth maps across Bangladesh using sparse in‑situ measurements and GLDAS groundwater storage data. The model achieves high predictive accuracy (R² ≈ 0.93) and generalizes to unseen years and districts, revealing a nationwide decline in groundwater recharge of 1.10 mm/yr and widespread deepening of water tables. The findings challenge the notion of widespread recharge enhancement from pumping and highlight a significant imbalance between recharge declines and increases.
arXiv:2607. 21559v1 Announce Type: new Abstract: A consensus anomaly detection framework was applied to monthly malaria surveillance data from Ghana (2014-2023) to identify atypical transmission patterns.
arXiv:2608. 19801v1 Announce Type: new Abstract: Financial anomaly detection often relies on large unlabeled transaction logs, where anomalous samples may already be present during training.