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    Drought vulnerability assessment using morphometric features and extreme precipitation indicators to prioritize sub-basins: AI-based Fuzzy Logic approach
    (2026-03-01)
    Nigam, Utkarsh
    ;
    Patel, Vinodkumar M.
    ;
    Patel, Dhruvesh P.
    ;
    Jodhani, Keval H.
    ;
    Gupta, Nitesh
    The identification of watersheds and extraction of drainage networks are essential for effective hydrological and geomorphological modelling. This study investigates the influence of morphometric factors and extreme precipitation events on the hydrological responses of the Sabarmati River Basin (SRB), India, to identify the drought-vulnerable sub-basins. Watershed prioritization was carried out using satellite remote sensing, GIS, and secondary data, including topographic sheets and ASTER DEM with a spatial resolution of 90 m. The SRB was divided into nine sub-watersheds, and 28 morphometric parameters were evaluated, comprising 07 linear, 15 areal, and 06 relief parameters. A compound factor (CF) was derived using multi-criteria decision-making techniques such as Weighted Sum Analysis (WSA), Principal Component Analysis (PCA), Analytic Hierarchy Process (AHP), Fuzzy-AHP (FAHP), and TOPSIS. Sub-watersheds were ranked based on CF value, where a lower CF indicated higher priority for runoff management strategies. Additionally, 42 years of precipitation data were analysed using the Standardized Precipitation Index (SPI) at timescales ranging from 3 to 24 months to assess trends in drought and extreme precipitation events. The analysis indicate decline in runoff potential in several sub-basins, however others (e.g., SB2, SB3, SB4, SB5) exhibit positive precipitation trends, making them suitable for runoff enhancement. This integrated methodology offers a comprehensive framework for managing sub-basins, optimizing runoff potential, and supporting sustainable water conservation. The results provide actionable insights for policymakers and planners to better utilize the SRB water resources based on its geomorphological and climatic characteristics.
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    AHP approach for employee recruitment with COVID-19 situation in Thailand
    (2026-01-01)
    Narabin, Akan
    ;
    Boonjing, Veera
    ;
    Wuttanachamsri, Kanognudge
    With the COVID-19 situation being unlike the usual, selecting an appropriate person for a job position requires the criteria and weight of each criterion be determined and adjusted to suit the crisis. The criteria chosen in this work are emphasised on selecting applicants who, while studying, have been in the midst of the COVID-19 situation. In this research, we employ analytic hierarchy process to assist the committee to have an agreement. In this study, each person in the committee can have his/her own pairwise comparison matrix of the criteria with a three-level hierarchical model. Some ambiguity may occur when initiating a hierarchical model; therefore, in this work, the criteria used in the three-level model are properly adjusted to create a four-level hierarchical model. The comparison inspected, provide some guidance in both theoretical manner and applications.
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    Application of EFA and AHP in the Last-Mile Delivery Service in Thailand
    (2023-01-01)
    Rattanakijsuntorn, W.
    Significant growth in electronic commerce in the 2010s has led to substantial expansion in last-mile delivery service for packages. Shifts in people's daily lifestyles also stimulate demand for the delivery of groceries and ready-to-consume meals. While the increasing number of last-mile delivery service providers offers customers more alternatives, the decision-making process involves more factors than just price and time. This paper aims to reveal factors affecting the decision to select last-mile delivery service providers by using exploratory factor analysis and analytical hierarchy process. Data were collected in two periods for differential analysis. It has been found that apart from price, quality, and time, customers are also concerned about the safety of online transactions and service innovation. Alarmingly, the environment is the least influential decision factor, leading to recommendations to further investigation on green practices awareness in the industry.