Assessment and prediction of Water Quality Index (WQI) by seasonal key water parameters in a coastal city: application of machine learning models.

The Water Quality Index (WQI) provides comprehensive assessments in river systems; however, its calculation involves numerous water quality parameters, costly in sample collection and laboratory analysis. The study aimed to determine key water parameters and the most reliable models, considering seasonal variations in the water environment, to maximize the precision of WQI prediction by a minimal set of water parameters. Ten statistical or machine learning models were developed to predict the WQI over four seasons using water quality dataset collected in a coastal city adjacent to the Yellow Sea in China, based on which the key water parameters were identified and the variations were assessed by the Seasonal-Trend decomposition procedure based on Loess (STL).

Assessing and predicting water quality index with key water parameters by machine learning models in coastal cities, China.

The water quality index (WQI) is a widely used tool for comprehensive assessment of river environments. However, its calculation involves numerous water quality parameters, making sample collection and laboratory analysis time-consuming and costly. This study aimed to identify key water parameters and the most reliable prediction models that could provide maximum accuracy using minimal indicators.

A flexible tactile sensor based on piezoresistive thin film for 3D force detection.

This paper presents a flexible tactile sensor with a compact structure based on a piezoresistive thin film and an elastomer for detecting three-dimensional (3D) force. The film contains four independent sensing cells, which were made using a type of piezoresistive ink and a specific pectinate conductive circuit pattern based on the flexible substrate to decrease the coupling effect. The elastomer with a spherical surface is bonded to the surface of the film and transfers the force to the sensing array.