TL;DR
A transformer-driven stacking ensemble framework for multiclass soil contamination classification that is optimized using Genetic Algorithms (GA) in order to overcome difficulties and make the predictions easier to understand and more aware of risks is suggested.
The proposed framework offers a robust, scalable, and interpretable solution for proactive soil health management and efficient fertilizer utilization, contributing toward sustainable agricultural practices as well as reliable and actionable decision support for precision agriculture.
This study provides a rigorous, data-driven framework for identification and spatially targeted management of groundwater heavy metal contamination in karst regions and offers a methodological reference for similar hydrogeological environments worldwide.
Aquaculture is a vital element of global food security and economic sustainability. The changing environment from time to time and the rapid spread of diseases in aquaculture leading to huge economic losses, still pose a major challenge in the maintenance of healthy fish. Timely intervention for rapid and accurate fish...
Sulfate attack progressively deteriorates concrete in marine, saline-soil, and sulfate-rich environments. This study developed an interpretable machine-learning and multi-objective optimization framework for sulfate-resistance-oriented concrete design. A literature-based dataset containing 744 records from 21 publicati...
Water distribution network (WDN) pipelines are essential infrastructure, and their failures cause significant economic and operational losses. Existing condition prediction models often struggle with high-dimensional non-linearities, severe class imbalance, and a lack of decision-centric interpretability. To address th...
Per- and polyfluoroalkyl substances (PFAS) are persistent groundwater contaminants that pose long-term risks to water sources and public health. Predicting PFAS occurrence remains challenging due to high-dimensional environmental data and limited model interpretability. In this study, we benchmarked explainable machine...
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