
<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">JongCheol Pyo</style></author><author><style face="normal" font="default" size="100%">Yakov Pachepsky</style></author><author><style face="normal" font="default" size="100%">Soobin Kim</style></author><author><style face="normal" font="default" size="100%">Ather Abbas</style></author><author><style face="normal" font="default" size="100%">Minjeong Kim</style></author><author><style face="normal" font="default" size="100%">Yongsung Kwon</style></author><author><style face="normal" font="default" size="100%">Mayzonee Ligaray</style></author><author><style face="normal" font="default" size="100%">Kyung Hwa Cho</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Long short-term memory models of water quality in inland water environments</style></title><secondary-title><style face="normal" font="default" size="100%">Water Research X</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.wroa.2023.100207</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">100207</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Water quality is substantially influenced by a multitude of dynamic and interrelated variables, including climate conditions, landuse and seasonal changes. Deep learning models have demonstrated predictive power of water quality due to the superior ability to automatically learn complex patterns and relationships from variables. Long short-term memory (LSTM), one of deep learning models for water quality prediction, is a type of recurrent neural network that can account for longer-term traits of time-dependent data. It is the most widely applied network used to predict the time series of water quality variables. First, we reviewed applications of a standalone LSTM and discussed its calculation time, prediction accuracy, and good robustness with process-driven numerical models and the other machine learning. This review was expanded into the LSTM model with data pre-processing techniques, including the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise method and Synchrosqueezed Wavelet Transform. The review then focused on the coupling of LSTM with a convolutional neural network, attention network, and transfer learning. The coupled networks demonstrated their performance over the standalone LSTM model. We also emphasized the influence of the static variables in the model and used the transformation method on the dataset. Outlook and further challenges were addressed. The outlook for research and application of LSTM in hydrology concludes the review.</style></abstract></record></records></xml>