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One of the key functions of global water resource management authorities is river water quality (WQ) assessment. A water quality index (WQI) is developed for water assessments considering numerous quality-related variables. WQI assessments typically take a long time and are prone to errors during sub-indices generation. This can be tackled through the latest machine learning (ML) techniques renowned for superior accuracy. In this study, water samples were taken from the wells in the study area (North Pakistan) to develop WQI prediction models. Four standalone algorithms, i.e., random trees (RT), random forest (RF), M5P, and reduced error pruning tree (REPT), were used in this study. In addition, 12 hybrid data-mining algorithms (a combination of standalone, bagging (BA), cross-validation parameter selection (CVPS), and randomizable filtered classification (RFC)) were also used. Using the 10-fold cross-validation technique, the data were separated into two groups (70:30) for algorithm creation. Ten random input permutations were created using Pearson correlation coefficients to identify the best possible combination of datasets for improving the algorithm prediction. The variables with very low correlations performed poorly, whereas hybrid algorithms increased the prediction capability of numerous standalone algorithms. Hybrid RT-Artificial Neural Network (RT-ANN) with RMSE = 2.319, MAE = 2.248, NSE = 0.945, and PBIAS = −0.64 outperformed all other algorithms. Most algorithms overestimated WQI values except for BA-RF, RF, BA-REPT, REPT, RFC-M5P, RFC-REPT, and ANN- Adaptive Network-Based Fuzzy Inference System (ANFIS).

The international services literature lacks research investigating the association between corporate size and corporate international experience with the internationalization. Using an integrated theoretical approach, this paper examines the internationalization of construction service corporations (CSCs) with specific emphasis on four dimensions: motives, firm-specific competitive advantages, home country-specific competitive advantages and locational considerations. A quantitative approach was adopted through a postal questionnaire survey. Out of 126 CSCS, 84 usable responses were analyzed giving 66.7% response rate. A comparative analysis was conducted using Mann-Whitney U test between the corporations having different sizes and international experiences. The findings show that CSCs of varied sizes did not concur over several firm-specific competitive advantages and home country-specific competitive advantages, whereas CSCs of varied international experiences were in disagreement over some locational factors. Small CSCs need to form joint ventures or partnerships in order to overcome their limitations. The government support is direly needed to promote internationalization of CSCs especially the younger and smaller ones which stand more vulnerable due to lack of opportunities in their domestic markets. The study will be helpful to managers and regulatory bodies towards creating a balanced playing field to develop the internationalization process for their service corporations.