A Data-Driven Machine Learning Framework for Multi-Criteria ESG Evaluation

Employee performance evaluation is a cornerstone of strategic human resource management, enabling organisations to align individual contributions with corporate objectives and sustain competitive advantage. In many manufacturing enterprises, appraisal systems remain generic in design, rely on simple additive scoring, and lack longitudinal tracking, thereby failing to capture the nuanced dynamics of specialised functional units. This study proposes an objective, department-specific evaluation framework integrating the Criteria Importance Through Intercriteria Correlation (CRITIC) weighting method with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) ranking approach. The framework was developed for the warehouse management division of a large electronics contract manufacturer and applied across twelve consecutive monthly evaluation cycles. A panel of eleven senior managers defined a performance instrument comprising ten criteria and thirty-one sub-criteria. CRITIC objectively assigned criterion weights from the statistical variability and intercorrelation of scores, eliminating subjective bias. TOPSIS produced defensible employee performance rankings by measuring proximity to the ideal solution. Professional skills and leadership consistently received the two highest weights, jointly accounting for approximately 35% of the total weighting. Longitudinal tracking revealed meaningful performance trajectories supporting targeted talent development and intervention decisions. The framework has been formally adopted by the case company as its instrument for operational performance evaluation.

Accurate prediction of significant wave height (SWH) is central to coastal ocean dynamics, wave–climate assessment, and operational marine forecasting, yet many high-performing machine-learning (ML) models remain opaque and weakly connected to underlying wave physics. We propose an explainable, feature engineering-guided ML framework for coastal SWH prediction that combines extremal wave statistics, temporal descriptors, and SHAP-based interpretation. Using 30 min buoy observations from a high-energy, wave-dominated coastal site off Australia’s Gold Coast, we benchmarked seven regression models (Linear Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Regression, K-Nearest Neighbors, and Neural Networks) across four feature sets: (i) Base (Hmax, Tz, Tp, SST, peak direction), (ii) Base + Temporal (lags, rolling statistics, cyclical hour/month encodings), (iii) Base + a physics-informed Wave Height Ratio, WHR = Hmax/Hs, and (iv) Full (Base + Temporal + WHR). Model skill is evaluated for full-year, 1-month, and 10-day prediction windows. Performance was assessed using R2, RMSE, MAE, and bias metrics, with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) employed for multi-criteria ranking. Inclusion of WHR systematically improves performance, raising test R2 from a baseline range of ~0.85–0.95 to values exceeding 0.97 and reducing RMSE by up to 86%, with a Random Forest|Base + WHR configuration achieving the top TOPSIS score (1.000). SHAP analysis identifies WHR and lagged SWH as dominant predictors, linking model behavior to extremal sea states and short-term memory in the wave field. The proposed framework demonstrates how embedding simple, physically motivated features and explainable AI tools can transform black-box coastal wave predictors into transparent models suitable for geophysical fluid dynamics, coastal hazard assessment, and wave-energy applications.

A great deal of effort has been made on the development of approaches based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Little attention is, however, paid to how to couple water quality indicators and their officially-defined standards with consideration of inter-correlation among indicators when TOPSIS is applied for evaluating water quality. This study proposes an improved TOPSIS-based approach called the Informative Weighting and Ranking (TIWR) approach. It couples water quality indicators and associated standards over the entire process and considers inter-correlation among indicators using the Criteria Importance Through Inter-criteria Correlation (CRITIC) approach. The approach is applied to the water quality evaluations of the Shitoumenkou reservoir and the Lake Tai. Results suggest that it produces a delicate level Hi associated with water quality for an object/monitoring site, which avoids classifying several objects into the same typical level and makes them distinguishable. The TIWR approach agrees well with traditional approach when a level Hi is transformed to a typical level. In addition, it can avoid some unreasonable results obtained by traditional approach. These findings have implications for decision makers and researchers in applying the TIWR approach in water environment protection and management.

Toward improved siting of wind–solar hybrid farms: A novel framework integrating multi-criteria decision making, machine learning–driven feature selection, and spatial clustering

Crowdsourcing in simple words is the outsourcing of a task to an online market to be performed by a diverse group of crowds in order to utilize human intelligence. Due to online labor markets and performing parallel tasks, the crowdsourcing activity is time- and cost-efficient. During crowdsourcing activity, selecting the proper labeled tasks and assigning them to an appropriate worker are a challenge for everyone. A mechanism has been proposed in the current study for assigning the task to the workers. The proposed mechanism is a multicriteria-based task assignment (MBTA) mechanism for assigning the task to the most suitable worker. This mechanism uses approaches for weighting the criteria and ranking the workers. These MCDM methods are Criteria Importance Through Intercriteria Correlation (CRITIC) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Criteria have been made for the workers based on the identified features in the literature. Weight has been assigned to these selected features/criteria with the help of the CRITIC method. The TOPSIS method has been used for the evaluation of workers, with the help of which the ranking of workers is performed in order to get the most suitable worker for the selected tasks to be performed. The proposed work is novel in several ways; for example, the existing methods are mostly based on single criterion or some specific criteria, while this work is based on multiple criteria including all the important features. Furthermore, it is also identified from the literature that none of the authors used MCDM methods for task assignment in crowdsourcing before this research.

Bu çalışmada Türk sigorta sektöründe hayat/emeklilik branşında faaliyette bulunan sigorta şirketlerinin performanslarının çok kriterli karar verme (ÇKKV) tekniklerinden CRITIC (CRiteria Importance Through Intercriteria Correlation) ve TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) yöntemleri kullanılarak ölçülmesi amaçlanmıştır. Bu kapsamda Türk sigorta sektöründe faaliyet gösteren 18 hayat sigorta şirketinin 2010 – 2020 yılları arasındaki verileri ele alınmıştır. Analiz kapsamında CRITIC – TOPSIS bütünleşik modeli için Kısa Vadeli Borçlar/ Toplam Aktifler, Dönem Net Karı/ Toplam Aktifler, Cari Varlıklar / Kısa Vadeli Borçlar, Faaliyet Giderleri / Toplam Varlıklar, Gerçekleşen Hasar / Kazanılmış Prim (Net), Toplam Özkaynaklar / Toplam Aktifler, Vergi Öncesi Kar /Alınan Primler (Brüt), Net Kar / Toplam Özsermaye, Alınan Primler (Net) / Alınan Primler (Brüt) ve Teknik Kar-Zarar / Alınan Primler kriterleri seçilmiştir. Çalışmada seçilen kriterlerin öncelik ağırlıkları CRITIC yöntemi ile tespit edildikten sonra TOPSIS yöntemi ile sigorta şirketleri performans bakımından sıralanmıştır. Çalışma sonucunda ele alınan yıllar itibarıyla hayat sigorta şirketlerinin performanslarının belirlenmesinde en önemli kriterin Kısa Vadeli Borçlar/Toplam Aktifler olduğu tespit edilmiştir. Çalışma sonucunda ele alınan yıllar itibarıyla hayat sigorta şirketlerinin performanslarının belirlenmesinde en önemli kriterin Kısa Vadeli Borçlar/Toplam Aktifler olduğu tespit edilmiştir. Ayrıca Halk Hayat ve Emeklilik A.Ş. ile Ziraat Hayat ve Emeklilik A.Ş.’nin ele alınan yıllarda performans sıralamasında ilk sıralarda yer aldığı BNP Paribas Cardif Hayat A.Ş.’nin analize dahil edilen tüm yıllarda performans sıralamasında son sıralarda olduğu tespit edilmiştir.

This research paper proposes a framework utilizing multicriteria tools for optimal site selection of photovoltaic solar farms. A comparative analysis was conducted using three quantitative methods—CRITIC (criteria importance through intercriteria correlation), PCA (principal component analysis), and entropy—to obtain the weights for the selection process. The evaluation considered environmental, demographic, financial, meteorological, and performance system criteria. TOPSIS (technique for order preference by similarity to ideal solution) was employed to rank the alternatives based on their proximity to the ideal positive solution and distance from the ideal negative solution. The capital cities of the seven departments in the Colombian Caribbean region were selected for the assessment, characterized by high annual solar radiation, to evaluate the suitability of the proposed decision-making framework. The results demonstrated that Barranquilla consistently ranked in the top two across all methods, indicating its strong performance. Cartagena, for instance, fluctuated between first and third place, showing some stability but still influenced by the method used. In contrast, Sincelejo consistently ranked among the lowest positions. A sensitivity analysis with equal weight distribution confirmed the top-performing cities, though it also highlighted that the weight assignment method impacted the final rankings. Choosing the appropriate method for weight calculation depended on factors such as the diversity and interdependence of criteria, the availability of reliable data, and the desired sensitivity of the results. For instance, CRITIC captured inter-criteria correlation, while PCA focused on reducing dimensionality, and entropy emphasized the variability of information.

Purpose This study investigates the scope, intensity, and thematic structure of human capital (HC) disclosures in Islamic banks. It addresses the gap in understanding how HC narratives are constructed, benchmarked and communicated in faith-based financial institutions across diverse regulatory settings. Design/methodology/approach The study adopts a multi-method framework combining lexicon-based extraction, bidirectional encoder representations from transformers (BERT)-based sentence classification, criteria importance through intercriteria correlation (CRITIC) weighting, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) benchmarking and BERTopic modeling. The analysis is based on 638 annual reports from 86 Islamic banks across 21 countries (2015–2023). Findings Results reveal substantial heterogeneity in disclosure intensity and thematic focus across institutions and jurisdictions. Compensation and governance-related themes dominate reporting, while diversity, equity and inclusion and employee well-being remain underdisclosed. The COVID-19 pandemic triggered a sharp increase in health and safety reporting. Country-level rankings highlight Indonesia, Malaysia and Bangladesh as consistent leaders. Originality/value An artificial intelligence/machine learning-enabled, multi-method framework is developed to measure and interpret HC disclosure in Islamic banking by integrating transformer-based sentence classification with CRITIC-weighted benchmarking, TOPSIS ranking and topic modeling. The study extends automated disclosure analytics to a Shariah-compliant setting and offers a scalable approach for cross-jurisdictional comparability and governance insight.

Multi-criteria decision-making (MCDM) techniques play a crucial role in solving real-life problems with imprecision and uncertainty. The p, q, r-spherical fuzzy rough set (p, q, r-SFRS) is an important development over FS theory for flexible representation of hesitancy, membership, and non-membership degrees. In this paper, we present a nuanced decision-making approach named confidence levels. Moreover, p, q, r-spherical fuzzy rough Einstein Bonferroni ($ {C}_{p, q, r} $-SFREBOM) aggregation operators, such as the p, q, r-spherical fuzzy rough Einstein Bonferroni weighted geometric operator ($ {C}_{p, q, r} $-SFREBOMWG) and p, q, r-spherical fuzzy rough Einstein Bonferroni weighted average operator ($ {C}_{p, q, r} $-SFREBOMWA), plays a crucial role in providing a strong support for MCDM analysis. The method considers lower and upper approximations of alternatives and synthesizes judgments along criteria based on expert confidence levels. The operational laws, theorems, and properties of p, q, r-SFREBOMWG and q, r-SFREBOMWA are explained, showing the superiority and importance of the suggested work, providing more accurate results than existing approaches. Integrating the newly proposed operator with multi-decision-making techniques like criteria importance through intercriteria correlation (CRITIC) and combinative distance-based assessment (CODAS) is a unique approach. A case study was considered to validate the efficacy and usability of the proposed operators in prioritizing sustainable municipal solid waste treatment techniques with the CRITIC and CODAS techniques. The computed results were compared with approaches to further support the outcomes of the proposed work. The comparison was done with technique for order preference by similarity to ideal solution (TOPSIS), and weighted aggregated sum product assessment (WASPAS) to assess the proposed work's reliability. Additionally, comparative and sensitivity analyses were conducted to demonstrate the robustness and excellence of the $ {C}_{p, q, r} $-SFREBOM approach with that of conventional MCDM methods.

Hospital site selection affects hospital’s personnel, resources, environment, and even long term benefit and cost. Such a strategic decision must be made efficiently by considering many conflicting key criteria such as economic, environmental, and technical. In this study, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), EDAS (Evaluation based on Distance from Average Solution), and CODAS (COmbinative Distance-based ASsessment) methods which are distance-based Multi-Criteria Decision-Making (MCDM) methods are applied to the hospital site selection problem. The weights of the hospital site selection criteria are derived from CRITIC (CRiteria Importance Through Intercriteria Correlation) method whereas the complete ranking of the hospital site alternatives is obtained by using TOPSIS, EDAS, and CODAS methods. According to the results of the CRITIC method the most important criterion is ‘market conditions’. The other criteria follow this criterion are cost, transportation, geological factors, land strategy, financial support by the government, environmental consideration, and demographic consideration, respectively. The complete ranking of the hospital site alternatives is also same according to the three distance-based methods. So it can be concluded that these methods can be used instead of each other as an alternative method for the various problem structures and overcoming the disadvantages of these methods.

This research paper proposes an innovative mathematical approach for evaluating motorized wheelchairs by integrating Additive Ratio Assessment (ARAS) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, along with the CRITIC (Criteria Importance through Intercriteria Correlation) weighting approach. By taking into account several factors at once, the integration of various approaches seeks to improve the evaluation process's accuracy and dependability. In order to handle the situation, the modelled framework takes into account 14 possible wheelchairs and 7 factors. The CRITIC weighting approach evaluates the impact of the criteria, while the TOPSIS and ARAS methodologies separately calculate the performance rating to produce an ordering of the chosen wheelchairs. Wheelchairs are evaluated using an array of factors, including recommendations from experts as well as online B2B market information. A Spearman’s correlation coefficient of 0.9696 confirms strong consistency between ARAS and TOPSIS based rankings The MW-12 variant stands out as the best option. The sensitivity evaluation was performed to test robustness of the proposed method. Practically, this integrated approach equips manufacturers with datadriven product development insights, aids healthcare providers in transparent procurement, and empowers end users and caregivers to select wheelchairs that best meet individual mobility needs.

Machine learning framework for predicting failure mode and shear capacity of ultra high performance concrete beams

Sustainable management of groundwater resource is a most critical due to its over exploitation and ascending stress by industrial and socio-economic factors. It is utmost important to manage this precious resource by properly identifying the suitable Groundwater Potential Zones (GPZ). Therefore, the main aim of the present study is to delineate the GPZ in the upper Godavari sub-basin of India by employing different bi-variate, Multi Criteria Decision Making (MCDM), ensembled, and Machine Learning (ML) models. These models include Weight of Evidence (WoE) (bi-variate), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) (MCDM), Fuzzified Functional Ratio (F-FR) (bi-variate), Extreme Gradient Boosting (XGB) (ML) and Extremely randomized Trees (ET) ML. The ensembled model featured different combination of WoE, TOPSIS and F-FR for mapping the enhanced accuracy in predicting the GPZ. A total of 15 groundwater factors were considered where 75% of the data were selected as training data and the rest 25% as validation data. These data were used to produce the ensembled ML models. The result of the model was plotted in terms of area under curve (AUC)-Receiver Operating Characteristics (ROC) curve and selected best model. The AUC-ROC result of the obtained model was found to be WoE oE models. The result of the model was plotted in termWoE_TOPSIS = 94%, WoE_F-FR = 93%, TOPSIS_F-FR = 94% and WoE_TOPSIS_F-FR = 95%. Results clearly indicate the improved accuracy of ensembled bi-variate and MCDM model over advanced ML model. The predicted statistical properties of the ensembled model also resembled ML models and a high correlation was observed. Thus, the ensembled model can be used over advanced ML models for delineating the GPZ mapping.

Deepwater oil and gas facilities typically encounter on an average up to 5% annual production losses due to unplanned downtime, conservatively estimated at billions of dollars impact for the industry. The existing toolkit and systems in place are not always adequate to identify and predict abnormal events that could lead towards unplanned facility shutdown. The interaction amongst process sub-systems and disturbances that propagate across these sub-systems with changing operating conditions are hard to predict without a fit-for-purpose model (or a digital twin). The focus of current work is on deepwater facility having several oil export pipeline pumps in parallel and several gas compressors in series. The alarm database showed records of several unplanned shutdown events around these critical equipements that resulted in undesirable outcomes such as production deferment, complete facility shutdown, loss of sales volumes and increased operational costs. In this work, an intelligent prognostic solution is proposed using machine learning (ML) framework for automatic prediction of impending facility downtime, and identification of key causative process variables. A systematic workflow was developed to identify, cleanse and process real time data for both model training and prediction. Several ML methods were evaluated; anomaly detection based on Principal Component Analysis (PCA) and Autoencoder (AE) algorithms were found performing better for the type of data available for the deepwater facility. The ML framework also supported analysis of underlying downtime causes to propose suitable mitigation steps. Knowledge based on physical understanding of the process was used to select each sub-system boundary and sensor list on which ML model was trained. These models were then cross-validated to test the accuracy of trained models. Finally, the alarm database was used to confirm the accuracy of the machine leaning models and identify root-causes for unplanned shutdowns. If the operating condition changes over time, the anomaly detection based ML models were setup to adapt to changing conditions by automatic model updates, resulting in significant reduction in false alarms. The adaptive ML models, when applied to one of the sub-system (with 30 different sensor data), predicted 24 unplanned events in 6 months of period, while when applied to another sub-system (with 40 sensor data), predicted only 6 unplanned downtime events. Several predictions were found as early as 30 mins to 2 hours, providing adequate early warning to take proactive actions. Case studies shown in the paper present diagnostic charts and identified early indicators were found in agreement with pre-alarms generated by existing alarm system, thus validating the ML solution. Current toolkit available to identify anomalous process behavior is limited to exception based surveillance with fixed min-max limits on each sensor data. Therefore, proposed adaptive ML solution has shown potential to revolutionize the topside process surveillance. This paper also describes how the ML framework can be scaled for a sustainable solution that provides prediction every minute, keeps the model evergreen utilizing cloud-based model deployment platform to train, predict and trigger automatic model updates and also span multiple process systems and facilities. Finally, we present directions for future work, where the current model can keep predicting various events and over time when sufficient events are collected, more advanced machine learning methods based on supervised ML can be developed and deployed.

Water resource health is one of the necessary conditions for society to achieve sustainable development. Due to the predominant focus of most studies on relatively short time spans, with limited attention to long time series and spatial trends, this study, using various regions of Henan Province as a case study, constructs a water resource security assessment framework based on the DPSIR model encompassing Drivers (D), Pressures (P), State (S), Impact (I), and Response (R) dimensions, with a selection of 19 evaluation indicators. Based on this evaluation index system, the CRITIC-TOPSIS evaluation method is formulated by integrating the CRITIC (Criteria Importance Through Intercriteria Correlation) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) models. This method is employed to assess the degree of water resource security in Henan Province from 2013 to 2022. And the Obstruction Degree Model is introduced to diagnose the water resource security levels in various regions of Henan Province. The assessment results indicate that over the past decade, the overall level of water resource security in various regions of Henan Province has shown an increasing trend. Irrigated area, per capita water resources, water consumption per unit of industrial value added, per acre water consumption for agricultural irrigation, the ratio of river length meeting water quality standards, groundwater supply proportion, and sewage treatment rate are identified as the primary obstacles influencing the water resource security levels in different regions of Henan Province. The research outcomes of this study can serve as theoretical foundations to enhance urban water resource security globally, ultimately facilitating sustainable development.