Development of an efficient water quality model using cutting-edge artificial intelligence techniques

For achieving the target level of satisfaction of water quality, several tools and techniques are utilized. The water quality index model is one of the widely used techniques. Recently, this approach has received much criticism in terms of model reliability and inconsistence assessment results. Since the development of the WQI model in 1965, the model's application has increased tremendously due to its simple mathematical architecture and ease of application. Many studies have
revealed that the existing technique produces a significant amount of uncertainty in the final assessment. In order to obtain reliability and consistency of assessment results, it should be optimized and improved, considering the existing limitations of the model. Therefore, here, we present the Irish Water Quality Index (IEWQI) model for assessing transitional and coastal water quality in an effort to improve the method and develop a tool that can be used by environmental regulators to abate water pollution in Ireland. The developed model has been associated with the adoption of water quality standards formulated for coastal and transitional waterbodies according to the water framework directive legislation by the environmental regulator of Irish water. The model consists of five identical components, including (i) indicator selection technique to select the crucial water quality indicator; (ii) sub-index (SI) function for rescaling various water quality indicators' information into a uniform scale; (iii) indicators' weight method for estimating the weight values based on the relative significance of real-time information on water quality; (iii) aggregation function for computing the water quality index (WQI) score; and (v) score interpretation scheme for assessing the state of water quality. Each component of the model has been tested and validated using cutting-edge artificial intelligence and machine learning techniques in order to avoid the intervention of experts or humans in terms of reducing model uncertainty. The findings of this study reveal that the IEWQI model could be an efficient and reliable technique for the assessment of transitional and coastal water quality more accurately in any geospatial domain.