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 ~98% to ~99% whereas AutoDL method yielded ~96% to ~98%. However, the AutoDL model ease the task of finding the appropriate DL model and proved better efficiency without manual intervention.

A review on recent advancements in recovery of valuable and toxic metals from e-waste using bioleaching approach

This review is intent on the environmental pollution generated from printed circuit boards and the methods employed to retrieve valuable and hazardous metals present in the e-wastes. Printed circuit boards are the key components in the electronic devices and considered as huge e-pollutants in polluting our surroundings and the environment as a whole. Composing of toxic heavy metals, it causes serious health effects to the plants, animals and humans in the environment. A number of chemical, biological and physical approaches were carried out to recover the precious metals and to remove the hazardous metals from the environment. Chemical leaching is one of the conventional PCBs recycling methods which was carried out by using different organic solvents and chemicals. Need of high cost for execution, generation of secondary wastes in the conventional methods, forces to discover the advanced recycling methods such as hydrometallurgical, bio-metallurgical and bioleaching processes to retrieve the valuable metals generate through e-wastes. Among them, bioleaching process gain extra priority due to its higher efficiency of metal recovery from printed circuit boards. There are different classes of microorganisms have been utilized for precious metal recovery from the PCBs through bioleaching process such as chemolithoautotrophy, heterotrophy and different fungal species including Aspergillus sp. and Penicillium sp. The current status and scope for further studies in printed circuit boards recycling are discussed in this review.

Recent advancements in the removal/recovery of toxic metals from aquatic system using flotation techniques

The paramount cause of water scarcity is pollution, which is becoming a massive issue since the last century. Besides, it is evident that water pollution is the main cause of emerging contaminants that are left untreated from industries, can cause serious threats to humans and biota as well. One of the best ways in remediating pollutants and finding a way for generating useable water is to use this contaminated water after the necessary treatment. Heavy metals are of major concern in treatment because of their toxicity, non-biodegradability, carcinogenicity, and they can cause inevitable damages even at low concentrations. In this review article, available different flotation techniques are discussed to address this issue. Flotation tends to be one of the promising techniques that have shown a high scope because of its high produce, low sludge formation, and ease of operation. From the several pieces of literature, it can be inferred that the flotation process can be conducted in one step, and that does not need any expensive materials. Further, this paper deliberates the versatility of each process in disclosing its advantages, limitations, further scope of research and fills the loopholes in the process for better effectiveness. Overall, flotation is a highly probable as well as effective treatment technology to eradicate noxious pollutants present in wastewater and thus helps to compromise environmental and social sustainability.

Automating water quality analysis using ML and auto ML techniques

Generation of unprocessed effluents, municipal refuse, factory wastes, junking of compostable and non-compostable effluents has hugely contaminated nature-provided water bodies like rivers, lakes and ponds. Therefore, there is a necessity to look into the water standards before the usage. This is a problem that can greatly benefit from Artificial Intelligence (AI). Traditional methods require human inspection and is time consuming. Automatic Machine Learning (AutoML) facilities supply machine learning with push of a button, or, on a minimum level, ensure to retain algorithm execution, data pipelines, and code, generally, are kept from sight and are anticipated to be the stepping stone for normalising AI. However, it is still a field under research. This work aims to recognize the areas where an AutoML system falls short or outperforms a traditional expert system built by data scientists. Keeping this as the motive, this work dives into the Machine Learning (ML) algorithms for comparing AutoML and an expert architecture built by the authors for Water Quality Assessment to evaluate the Water Quality Index, which gives the general water quality, and the Water Quality Class, a term classified on the basis of the Water Quality Index. The results prove that the accuracy of AutoML and TPOT was 1.4 % higher than conventional ML techniques for binary class water data. For Multi class water data, AutoML was 0.5 % higher and TPOT was 0.6% higher than conventional ML techniques.