Landslides represent a recurrent hazard in tropical mountain environments, where rapid urbanization and extreme rainfall amplify disaster risk. The Sentani region of Papua, Indonesia, is highly vulnerable, as demonstrated by the catastrophic debris flows of March 2019 that caused fatalities and widespread losses. This study developed high-resolution landslide susceptibility maps for Sentani using an ensemble machine learning framework. Three base learners—Random Forest, eXtreme Gradient Boosting (XGBoost), and CatBoost—were combined through a logistic regression meta-learner. Predictor redundancy was controlled using Pearson correlation and Variance Inflation Factor/Tolerance (VIF/TOL). The landslide inventory was constructed from multitemporal satellite imagery, integrating geological, topographic, hydrological, environmental, and seismic factors. Results showed that lithology, Slope Length and Steepness Factor (LS Factor), and earthquake density consistently dominated model predictions. The ensemble achieved the most balanced predictive performance, Area Under the Curve (AUC) > 0.96, and generated susceptibility maps that aligned closely with observed landslide occurrences. SHapley Additive Explanations (SHAP) analyses provided transparent, case-specific insights into the directional influence of key factors. Collectively, the findings highlight both the robustness and interpretability of ensemble learning for landslide susceptibility mapping, offering actionable evidence to support disaster preparedness, land-use planning, and sustainable development in Papua.

Intense chemical weathering in tropical environments poses challenges for conventional geochemical exploration, as primary lithological signatures become heavily altered. Stream sediment geochemistry provides a robust alternative for detecting anomalous geochemical patterns under these conditions. In this study, 636 stream sediment samples and 15 rock samples were evaluated using Principal Component Analysis (PCA), Median + 2 Median Absolute Deviation (MAD), and Concentration–Area (C–A) fractal modeling to identify potential anomaly zones. These results were compared with the traditional Mean plus 2 Standard Deviation (SD) approach. The findings indicated that Mean + 2SD offers a conservative threshold but overlooks anomalies in heterogeneous datasets, while Median + 2MAD provides robustness against outliers. The C-A fractal model effectively characterizes low- and high-order anomalies by capturing multiscale variability. Elements such as Au–Ag–Hg–Se–Sb–As form a system indicating low- to intermediate-sulphated epithermal mineralization. Au–Pb points to polymetallic hydrothermal mineralization along intrusive contacts. The southern region is a primary mineralization center controlled by an intrusive–volcanic boundary, whereas the east and west areas exhibit secondary mineralization, characterized by altered lava breccia. The correlation between shallow epithermal and deeper intrusive-related porphyry systems, especially regarding Au–Ag, offers new insights into the metallogenic landscape of the Sunda–Banda arc. Beyond regional significance, this research presents a geostatistical workflow designed to mitigate exploration uncertainty in geochemically complex zones, providing a structured approach applicable to volcanic-arc mineralized provinces worldwide.

The effect of compost on mercury content in sweet potatoes grown on soil contaminated by mine tailings was investigated in this study. In a completely randomized factorial design with three repetitions, the sweet potato MZ119 clone was planted on soil with a mixture of tailings at a ratio of 0%, 30%, 50%, 70%, and 100% (w/w) and compost amendment (0 g/pot, 250 g/pot, 500 g/pot, and 750 g/pot). An increase in the tailing contamination ratio led to an increase in mercury accumulation in sweet potatoes. In contrast, an increase in compost dosage reduced mercury accumulation. Compost doses of 500 and 750 g/pot were most effective in reducing mercury accumulation in sweet potatoes. The mercury concentration in sweet potato tubers ranged from 0.153 to 0.802 mg/kg, which is above the threshold required for crops set by the WHO/FAO and other international standards. However, sweet potato plants exhibited a high mercury accumulation potential for mercury phytoremediation purposes, as they can accumulate mercury up to 18.15 mg/kg in their leaves.