Multiple machine learning algorithms assisted QSPR models for aqueous solubility: Comprehensive assessment with CRITIC-TOPSIS

Assessment of watershed health, integrating environmental, social, and climate change criteria into a fuzzy logic framework

Spatio-temporal analyses of environmental and social criteria in the context of climate change, facilitate understanding of how historical and current conditions have influenced watershed health. Previous studies have analyzed watershed health, but very few have integrated fuzzy logic with the CRITIC method (Criteria Importance Through Intercriteria Correlation), which enables us to explore alternatives to improve watershed performance. The objective of this study was to evaluate changes in watershed health through historical and projected climate change scenario in the tropical Santa Cruz watershed in Aquismón, S.L.P., Mexico (1985-2027) considering environmental criteria (hydrological and sediment connectivity, runoff, flooding, drought, landscape fragmentation) and social criteria (indigenous population density, human impact on biodiversity, health index, income index, education index). The results indicate that spatio-temporal changes can alter the Watershed Health Score (WHS) from a value of 2.69 to 6.90, particularly in areas with precarious social conditions. Moreover, the study reveals how weighting evolves overt time, as seen in the case of landscape fragmentation, whose value increased 0.0113 to 0.254. This study shows how objective methods such as CRITIC can be integrated through fuzzy logic to analyze the spatio-temporal evolution of watershed problems without the need for a large number of experts to weight the variables (subjective methods). This method can subsequently be applied and reproduced in different zones or watersheds where there is no certainty as to which criteria have the greatest influence and thus enable decisions for watershed management or restoration.