A systematic literature review on lake water level prediction models. (May 2023)

• Record Type: Journal Article
• Authors: Ozdemir, Serkan; Yaqub, Muhammad; Yildirim, Sevgi Ozkan
• Is Part Of: Environmental modelling & software. Volume 163(2023)
• Publication Date: 2023-05
• Subjects: Lake water level -- Reservoir water level -- Prediction -- Forecast -- Time series; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2023.105684
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Global climate change has led to large fluctuations in lake levels in recent years as meteorological and hydrological parameters have changed and water use has been intense. Water scientists use various computer models to analyze the hydrological variables recorded in the past and make projections for all future scenarios. Based on the technological progress, six different types of algorithms were studied in this review to predict the water level in lakes. The prediction results show that Deep Learning (DL) has the highest accuracy in terms of the evaluation metrics. Since the Artificial Intelligence (AI) field is still emerging and continue to improve, this study highlights better comprehension of current applications and the problems that need to be investigated more for LWL forecasting techniques. It reveals that the studies mainly focused on lakes either in USA or China and there is room for improvement for other locations that are scarcely investigated. Highlights: This paper presents a systematic literature review for lake and reservoir water level studies for the first time. Researchers favored input parameters as lake water level, precipitation and temperature among others. The studies can be extended towards the direction of least studied areas such as deep learning based algorithms. Increasing water level prediction areas recently indicates it became a hot topic because of visible climate change effects.