Selection of optimal aggregation function for the revised leachate pollution index (r-LPI)

Bisht, Tribhuwan Singh; Kumar, Dinesh; Alappat, Babu J.

doi:10.1007/s10661-022-09861-5

Cited by 10 publications

(4 citation statements)

References 33 publications

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“…It enables us to understand if the variance in the output can be attributed to variation in the input, either qualitatively or quantitively. A thorough investigation into the selection of appropriate aggregation functions was carried out and reported elsewhere (Bisht et al 2022b). As a result, the weighted arithmetic aggregation function was found to exhibit comparatively less eclipsing than the weighted multiplicative and is also sensitive to variations in the sub-index values and was thus used in the analysis (Bisht et al 2022b).…”

Section: Aggregation Of Sub-indicesmentioning

confidence: 99%

Revised Leachate Pollution Index (r-LPI): A Tool to Quantify Pollution Potential of Landfill Leachate

Kumar

Alappat

Bisht

2022

Preprint

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The leachate pollution index (LPI), a technique to quantify the contamination potential of landfill leachate, was developed in 2003. Since then, numerous factors have challenged the relevance of LPI, including advancements in technology, the long-term reliability of these indicators, the incidence of emerging contaminants, and the LPI’s efficacy. As a result, using LPI as a benchmark can lead to misinterpretation of the magnitude of leachate Pollution. To mitigate this, a revised leachate pollution index (r-LPI) was developed, which is more precise and robust in assessing the Pollution potential of landfill leachate. This article presents a comprehensive account of the development of r-LPI. The r-LPI was developed by incorporating fuzzy technique with a multi-criteria decision-making technique (MCDM), wherein the inputs from 60 experts in the field of the environment, specifically solid waste management, were acquired at different stages during its development. The fuzzy Delphi method (FDM) was used to select the parameters. The fuzzy analytic hierarchy process (FAHP) was used to compute the relative weights of the parameters and sub-index curves were used for normalization of the parameters. As an application, the LPI and the r-LPI of the Bhalswa, Okhla, and Ghazipur landfills were calculated. The results indicate that r-LPI provides a more comprehensive prediction of leachate Pollution than the LPI.

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Section: Aggregation Of Sub-indicesmentioning

confidence: 99%

Revised Leachate Pollution Index (r-LPI): A Tool to Quantify Pollution Potential of Landfill Leachate

Kumar

Alappat

Bisht

2022

Preprint

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“…As a multidimensional entity, a series of survey questionnaires were carried out at different stages to reduce the ambiguity and compensate for subjectivity in the development of a composite indicator such as r‐LPI. Selection of parameters, relative ranking, normalization, and parameter aggregation are the four stages that entail the formulation of an index 31,32 . In this study, three FAHP methods were used to calculate the weights of the r‐LPI parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Selection of parameters, relative ranking, normalization, and parameter aggregation are the four stages that entail the formulation of an index. 31,32 In this study, three FAHP methods were used to calculate the weights of the r-LPI parameters. The results of these methods were compared to identify the optimal method for the calculation of the weights of the r-LPI parameters.…”

mentioning

confidence: 99%

Deriving and comparing priority vectors for revised‐leachate pollution index (r‐LPI) using three fuzzy analytic hierarchy process

Bisht

Kumar

Alappat

2022

Env Prog and Sustain Energy

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The revised leachate pollution index (r-LPI) developed using the fuzzy Delphi analytical hierarchy process (FDAHP) is a technique to quantify the contamination potential of landfill leachate. The steps involved in the formulation of the r-LPI were the selection of parameters using the fuzzy Delphi method (FDM), computing relative weights of the parameters using the fuzzy analytic hierarchy process (FAHP), development of sub-index curves, and finally, aggregation of sub-indices. The calculation of the weights of the parameters has been one of the most crucial steps in the formulation of any index. The analytical hierarchy process (AHP) has been one of the most widely used methods for weight calculation. However, it was observed that the traditional AHP is inadequate to deal with the imprecise or ambiguous nature of linguistic evaluation. It also involves human subjectivity, which induces ambiguity and entails the use of decision-making with uncertainty. The fuzzy AHP method was used in this study to overcome this difficulty. Three significant issues pertinent and essential to fuzzy AHP were addressed: contradictions in expert judgment, deriving priorities from inconsistent fuzzy judgment matrices, and group decision-making. In this study, three FAHP methods: Chang's extent analysis (EA) method, fuzzy preference programming (FPP), and logarithmic fuzzy preference programming (LFPP) were used to calculate the weights of the r-LPI parameters. The results of these methods were then compared, and it was determined that LFPP was the most suitable method for calculating the weights of the r-LPI parameters.

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“…An in-depth experimental comparison of the first results obtained for this class of functions was presented, among others, in [36], [40]. In general, the problem of selection of optimal aggregation function has been also considered recently in [41]- [44].…”

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confidence: 99%

Quadrature-Inspired Generalized Choquet Integral in an Application to Classification Problems

Karczmarek,

Dolecki,

Powroznik

et al. 2023

IEEE Access

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Correct classification remains a challenge for researchers and practitioners developing algorithms. Even a minor enhancement in classification quality, for instance, can significantly boost the effectiveness of detecting conditions or anomalies in safety data. One solution to this challenge involves aggregating classification results. This process can be executed effectively as long as the aggregation function is appropriately chosen. One of the most efficient aggregation operators is the Choquet integral. Furthermore, there exist numerous generalizations and extensions of the Choquet integral in the existing literature. In this study, we conduct a comprehensive analysis and evaluation of a novel approach for deriving an aggregate classification. The aggregation process applied to various classifiers is based on enhancements to the Choquet integral. These novel expressions draw inspiration from Newton-Cotes quadratures and other well-known formulae from numerical analysis. In contrast to previous approaches that exploit the generalization of the Choquet integral, our approach requires the utilization of two or three adjacent values associated with the membership of a specific element in different classes. This enables the use of more efficient enhancements in terms of accuracy measurement. Specifically, the t-norm following the integral symbol can be effectively replaced by mathematical expressions used in executing numerical integration formulae. This leads to more precise results and aligns with the concept of numerical integration. Furthermore, in a series of experiments, we thoroughly assess the performance of the proposed approach in terms of classification accuracy. We analyze the strengths and weaknesses of the new approach and establish the experimental settings that can be applied to similar tasks. In the series of experiments, we have demonstrated that the proposed Quadrature-Inspired Generalized Choquet Integral (QIGCI) can either outperform previous enhancements of the Choquet integral or at least achieve a similar level of accuracy measurement. However, we also highlight scenarios where previous approaches can still be a suitable choice. The number of QIGCI-based aggregation models that outperform others is convincing, indicating that this approach is worthy of consideration. INDEX TERMSAggregation, anomaly detection, Choquet integral, generalized Choquet integral, numerical quadratures, many classifiers, pre-aggregation functions I. INTRODUCTION T HE The problem of classification has recently become one of the most significant challenges in theoretical and applied computer science. It arises in various contexts such as image analysis, anomaly detection, and tabular data recognition. Especially in the past decade, this task has seen

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Selection of optimal aggregation function for the revised leachate pollution index (r-LPI)

Cited by 10 publications

References 33 publications

Revised Leachate Pollution Index (r-LPI): A Tool to Quantify Pollution Potential of Landfill Leachate

Revised Leachate Pollution Index (r-LPI): A Tool to Quantify Pollution Potential of Landfill Leachate

Deriving and comparing priority vectors for revised‐leachate pollution index (r‐LPI) using three fuzzy analytic hierarchy process

Quadrature-Inspired Generalized Choquet Integral in an Application to Classification Problems

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