Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon

By Riccardo Taormina, Kwok-Wing Chau, Rajandrea Sethi. Engineering applications of artificial intelligence, 2012

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Reservoir flood control operation based on chaotic particle swarm optimization algorithm (2014, methodology): “With the development of the fusion of water conservancy engineering and computing science by artificial intelligence technology and intelligent optimization method [3–7], many scholars have begun to introduce various optimal algorithms into develop the study on the flood control operation [8–10].”
Short-term prediction of groundwater level using improved random forest regression with a combination of random features (2018, background): “Therefore, the physical domain needs to be partitioned to obtain a numerical solution (Taormina et al. 2012).”
Seasonal rainfall forecasting by adaptive network-based fuzzy inference system (ANFIS) using large scale climate signals (2016, methodology): “Among different training algorithms Levenberg–Marquardt (LM) algorithm is the most popular algorithm used in many hydrological studies (Raghuwanshi et al. 2006; Aqil et al. 2007; Taormina et al. 2012).”
Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression (2021, background): “Hence, in recent years, a number of studies used machine learning algorithms to predict GWL (Taormina et al., 2012; Porte et al., 2018; Lal & Datta, 2018; Su et al., 2020; Niu & Feng, 2021; CONTACT Amir Mosavi amir.mosavi@mailbox.tu-dresden.de; Shab S. Band shamshirbands@yuntech.edu.tw Alcalá…”