A comprehensive comparative analysis of deep learning tools for modeling failures in smart water taps

Abstract

Predicting early-stage failure in smart water taps (SWT) and selecting the most efficient tools to build failure prediction models are many challenges that water institutions face. In this study, three Deep Learning (DL) algorithms, i.e., the Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (BiLSTM), were selected to analyse and determine the most appropriate among them for failure prediction in SWTs. This study uses a historical dataset acquired from smart water withdrawal taps to determine the most efficient DL neural network architecture for failure prediction in the SWT, leading to a hybrid model's development. After a comprehensive evaluation of the three ML models, findings show that a hybrid combination of the CNN and Bi-LSTM (CNN-BiLSTM) models is a better solution for investigating failures in the SWT.

Prediction of land surface temperature of major coastal cities of India using bidirectional LSTM neural networks

“…Although Long Short-Term Memory (LSTM) is superior to traditional ML methods in processing large bulk of input data and has a relatively fast computational speed, LSTM is not always the best choice considering the accuracy of model predictions (Cai et al 2019;Wang et al 2019). Among them, the BiLSTM model has already been applied in the prediction of photovoltaic power output (Wang et al 2019), short-term load forecasting (He 2017), wind speed and solar radiation forecasting (Díaz-Vico et al 2017), water resources (Hu et al 2019;Offiong et al 2021), and stock-market predictions (Althelaya et al 2018). However, there is limited research on BiLSTM application in ST forecasting.…”