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All Journal AMPLITUDO: Journal of Science & Technology Innovation
Khaista Rehman
University of Peshawar
Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture

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A Hybrid Ensemble Framework for Probabilistic Earthquake Forecasting in Northern California in Support of SDG 11: Sustainable and Resilient Cities Madlazim Madlazim; Baba Musta; Aris Doyan; Adi Susilo; Khaista Rehman
AMPLITUDO : Journal of Science and Technology Innovation Vol. 5 No. 1 (2026): February
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/amplitudo.v5i1.496

Abstract

Forecasting earthquakes is still one of the most difficult problems in geophysics, mainly because seismic activity is irregular and often influenced by many factors that interact in complex ways. In this study, we develop a leakage-controlled hybrid ensemble model that combines CatBoost, LightGBM, XGBoost, and Gradient Boosting to predict five earthquake parameters: magnitude, depth, latitude, longitude, and a scaled inter-event interval in Northern California. These models were trained using USGS earthquake data ranging from 1900 to 2025 (M ≥ 4.0), with a process designed to prevent time leakage through strict time separation, a moving window feature, and prospective validation. Overall, the hybrid models produced consistently low MAE and RMSE values ​​and very high R² values ​​(above 0.99) for all target variables. While the estimates performed impressively, the results should be interpreted in a probabilistic context, with recognition of the inherent uncertainty of seismic processes. The framework proposed here provides a clear and replicable approach that can support the development of systems for more reliable short-term earthquake forecasting
Scientific Creativity and Argumentation in Inquiry-Based and Blended Science Learning: Implications for Quality Education (SDG 4) through an Integrative IAC Framework Dyah Permata Sari; Madlazim Madlazim; Mustaji Mustaji; Binar Kurnia Prahani; Baba Musta; Khaista Rehman; Yan Putra Timur
AMPLITUDO : Journal of Science and Technology Innovation Vol. 5 No. 2 (2026): August
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/amplitudo.v5i2.715

Abstract

This study systematically reviews the relationship between scientific creativity and scientific argumentation within inquiry-based and blended science learning, with implications for achieving Sustainable Development Goal 4. Following PRISMA 2020 principles, the review synthesizes 48 peer-reviewed studies published from 2015 to early 2026 across Scopus, Web of Science, and ERIC. The findings show that inquiry-based learning provides the epistemic foundation for both creativity and argumentation, while blended and digitally supported environments extend discourse, collaboration, and multimodal representation. Argumentation functions as a convergent epistemic filter that validates, refines, and strengthens creative scientific ideas. Based on these findings, the study proposes an integrative Inquiry-Argumentation-Creativity framework in which creativity is generated through inquiry and refined through evidence-based argumentation in blended environments. The synthesis suggests that future research should combine longitudinal designs with learning analytics, natural language processing, and multimodal analysis to better capture how creativity and argumentation develop over time.
Hybrid GNSS-Rainfall Decision Support for Staged Early Warning of Rainfall-Induced Slope Instability in Agricultural Landscapes: A Yarra Valley Prototype Supporting SDG 2 and SDG 11 Madlazim Madlazim; Muhammad Nurul Fahmi; Arie Realita; Baba Musta; Dyah Permata Sari; Khaista Rehman
AMPLITUDO : Journal of Science and Technology Innovation Vol. 5 No. 2 (2026): August
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/amplitudo.v5i2.736

Abstract

Early detection of rainfall-induced slope instability in agricultural terrain remains difficult because hydrological loading commonly precedes measurable ground movement. This study develops an explainable decision-support framework that integrates GNSS-derived detrended displacement, antecedent rainfall indicators, and a machine-learning anomaly score for staged warning in the Yarra Valley, Australia. A regional target-control GNSS architecture was implemented with four slope stations and two control stations, and a retrospective rainfall-driven event window in April 2025 with complete processing products was analysed. The workflow combines 24 h and 72 h rainfall accumulation, target-control displacement metrics, deformation gradients, and a weighted fusion index to distinguish background variability from physically plausible slope response. Cumulative rainfall increased before localized deformation emerged, and the lower slope sector showed stronger response than the upper sector. The fusion layer therefore supports escalation from normal to watch and, when rainfall and deformation thresholds are jointly exceeded, to warning. This prototype strengthens operational decision-making for agricultural slope management and provides a basis for multi-event validation and wider deployment
Co-Authors Adi Susilo Arie Realita Aris Doyan Baba Musta Binar Kurnia Prahani Dyah Permata Sari Dyah Permata Sari Madlazim Madlazim Muhammad Nurul Fahmi Mustaji Mustaji Yan Putra Timur