Mamdouh, M., Ezzat, M. and Hefny, H. (2024) Improving Flight Delays Prediction by Developing Attention-Based Bidirectional LSTM Network. Expert Systems with Applications, 238, Article 121747.
https://doi.org/10.1016/j.eswa.2023.121747

has been cited by the following article:

• TITLE: Prediction of Water Quality Temperature in the Growth Pattern of Fish (Nile Tilapia) and Plant (Lettuce) in a Prototype-Safe, Automated Aquaponics Environment Using Deep Learning

  AUTHORS: Richard Owusu, Jean Chrysostome Mayoko, Jae Young Lee

  KEYWORDS: Long-Short Term Memory (LSTM), Convolutional LSTM, Recurrent LSTM, Machine Learning, Deep Learning, Aquaponics, Nitrification, Water Quality, Recirculation

  JOURNAL NAME: Open Access Library Journal, Vol.11 No.9, September 25, 2024

  ABSTRACT: The increasing demand for water resources, decreased land water availability, and concerns about food security have led to the development of innovative food production methods, such as aquaponics. Fish tank organic waste is fed to plants for growth, with perforated water returning to fish, reducing water consumption and reusing water compared to traditional agricultural practices. This hybrid technology combines aquaculture with hydroponics, requiring constant monitoring of water quality parameters to prevent fish death and specific fish such as Catfish and Salmon and specific plants including Spinach, Cabbage, Kintonmire, and Cauliflower fit well in the aquaponics system. This research aims to build an aquaponics system using a machine learning tool for monitoring water quality temperature, reducing manual tasks, and improving accuracy. The findings show aquaponics as an ideal solution for food production, focusing on heat exchanges and water temperature. The Convolutional LSTM model performed better than the Recurrent neural network, predicting high scores of 96%, 98%, and 99% with different power levels after 200 epochs and a 64-batch batch size respectively 5, 10, and 15 watts.