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All Journal Frontiers in Sustainable Science and Technology Journal of Material Science and Radiation
Faqiri, Ahmad Fawad
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Materials Science & Nanotechnology Medicine & Pharmacology Physics Social Sciences

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Rainfall and Groundwater Relationship Assessment in the North River Basin, Afghanistan Faqiri, Ahmad Fawad; Safi, Abdul Ghias
Frontiers in Sustainable Science and Technology Vol. 2 No. 2 (2025): December
Publisher : CV. Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/fsst.v2i2.603

Abstract

Rainfall and groundwater variability are critical for sustainable water-resource management in arid and semi-arid regions like Afghanistan. This study investigates the relationship between precipitsation and groundwater levels in the North River Basin, using long-term rainfall data (1979–2022) from 13 meteorological stations and groundwater observations from 216 wells across five sub-river basins during 2022–2023. Descriptive statistics and the Precipitation Concentration Index (PCI) were applied to assess temporal rainfall patterns, seasonal variability, and rainfall concentration. Groundwater trends were analyzed by comparing monthly and annual fluctuations in water-table levels. Results indicate a decline in precipitation from 1979–1999, followed by gradual recovery after 2000, with rising rainfall concentration in recent decades, particularly in the dry season. Groundwater data reveal significant fluctuations across the sub-river basins, with some areas experiencing drawdown, likely due to over-extraction or insufficient recharge. Correlation analysis highlights the influence of rainfall variability on groundwater levels, demonstrating the importance of understanding seasonal recharge dynamics. These findings provide insights into the coupling between rainfall and groundwater, offering valuable information for water-resource planning, drought mitigation, and sustainable management of aquifers in the North River Basin.
Predictive Modeling of Geohazards Using Artificial Intelligence: Earthquakes, Landslides, and Volcanic Risk Assessment Faqiri, Ahmad Fawad; Faqiri, Nasrin; Hakimi, Musawer
Journal of Material Science and Radiation Vol. 2 No. 1 (2026)
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/jmsr.v2i1.706

Abstract

Geohazards such as earthquakes, landslides, and volcanic eruptions pose severe threats to human life and infrastructure, causing significant global losses every year. Existing hazard assessment methods are limited by single-hazard focus, high computational cost, sparse data integration, and poor real-time forecasting capabilities, which limit their operational use. This study aims to develop a unified artificial intelligence (AI) framework for multi-hazard forecasting by integrating convolutional neural networks (CNNs), long short-term memory (LSTM) models, random forest classifiers, and ensemble fusion techniques. A multi-source dataset consisting of seismic, geospatial, and geochemical data was processed using an 80/10/10 split train-validate-test, cross-validation, and spatial validation strategies. The results show strong performance, with earthquake classification AUC-ROC of 0.961, magnitude prediction RMSE of 0.23 Mw, landslide sensitivity AUC of 0.957, and volcanic classification accuracy of 91.2%, outperforming several state-of-the-art benchmarks. Ensemble fusion improved performance by 2.1–3.7% over individual models. The key contribution is a scalable ensemble-based AI framework that enables integrated multi-hazard forecasting on heterogeneous datasets. However, limitations include information heterogeneity and reduced cross-regional generalizability. The framework supports real-time early warning systems, disaster risk management, and land-use planning, especially in hazardous areas.
Co-Authors Faqiri, Nasrin Hakimi, Musawer Safi, Abdul Ghias