Analysis and prediction of water quality using deep learning and auto deep learning techniques

Natural water sources like ponds, lakes and rivers are facing a great threat because of activities like discharge of untreated industrial effluents, sewage water, wastes, etc. It is mandatory to examine the water quality to ensure that only safe water is available for consumption. Traditional methods of water quality inspection are a cumbersome process and hence, Artificial Intelligence (AI) can be used as a catalyst for this process. AutoDL is an upcoming field to automate deep learning pipelines and enables model creation and interpretation with minimal code. However, it is still in the nascent stage. This work explores the suitability of adopting AutoDL for Water Quality Assessment by drawing a comparison between AutoDL and a conventional models and analysis to foresee the quality of the water, an appropriate class based on Water Quality Index segregating water bodies into different classes. The accuracy of conventional DL is 1.8% higher than that of AutoDL for binary class water data. The accuracy of conventional DL is 1% higher than that of AutoDL for multiclass water data. The accuracy of conventional model was similar to 98% to similar to 99% whereas AutoDL method yielded similar to 96% to similar to 98%. However, the AutoDL model ease the task of finding the appropriate DL model and proved better efficiency without manual intervention.

Automated summary

This study explores the feasibility of using artificial intelligence for water quality assessment and compares traditional models with AutoDL models. The results show that the traditional models have slightly higher accuracy, but the AutoDL model is more efficient without the need for manual intervention.