• Roshini L

Real-time water quality monitoring using automated systems with sensors is becoming increasingly common, which enables and demands timely identification of unexpected values. Technical issues create anomalies, which at the rate of incoming data can prevent the manual detection of problematic data. My research work presents a review of improvement in water quality data by detecting anomalies using machine learning and static learning approaches. Models are applied to water quality data: logistic regression, linear discriminant analysis, support vector machines (SVM), artificial neural network (ANN), deep neural network (DNN), long short-term memory (LSTM),Reinforcement algorithm, manual DL approaches outperform traditional ML techniques in terms of implicit feature learning accuracy, fewer false positive rates,Extreme Learning Machine (ELM)limited in terms of feature extraction, owing to the single-hidden network layer structure.A hybrid model is formulated that takes advantage of both and it is data invariant too. DL approaches outperform traditional ML techniques in terms of feature learning accuracy and fewer false positive. My work propose a hybrid DL-ELM approach to WQAD to satisfy some of the desirable requirements that the algorithm be efficient in terms of accuracy time, anomaly detection time and memory computational complexities, as well as being able to handle high-dimensional data search space occupied by sensors.

"Development of Anomaly Detection Model for Water Quality by Using Machine Learning Techniques ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.b100-b114, March-2023, Available :http://www.jetir.org/papers/JETIR2303112.pdf

"Development of Anomaly Detection Model for Water Quality by Using Machine Learning Techniques ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppb100-b114, March-2023, Available at : http://www.jetir.org/papers/JETIR2303112.pdf