Artificial intelligence based models for stream-flow forecasting: 2000-2015

By Zaher Mundher Yaseen, Ahmed El-Shafie, Othman Jaafar, Haitham Abdulmohsin Afan, Khamis Naba Sayl. 2015

501 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/9a222f519165011866b2caea12dcb78d47312398

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In Environmental Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources (2019, methodology): “[32] Artificial neural networks, support vector machine, fuzzy logic, evolutionary computing, wavelet-artificial intelligence model Streamflow forecasting General evaluation of the algorithms”
Bootstrap rank‐ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling (2016, methodology): “We have adopted neural network based models in this work for mapping explanatory variables into process forecasts as these (nonlinear) methods are very common in water resources forecasting applications [Abrahart et al., 2012; Yaseen et al., 2015].”
Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction (2020, background): “…of the drainage basin, rainfall amount, intensity, and its distribution (Ochoa-Tocachi et al. 2016; Teutschbein et al. 2018), while data-driven models construct the mathematical relationship (linear or non-linear) between the streamflow and its constraints (Yaseen et al. 2015; Zhang et al. 2018).”
Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment (2018, methodology): “There are five widely employed types of AI approaches based on a recent review conducted by Yaseen et al. (2015) used to forecast river flow, including the artificial neural network (ANN), support vector machine (SVM), fuzzy set, evolutionary computation (EC), the wavelet-artificial intelligence…”
Monthly runoff forecasting based on LSTM–ALO model (2018, methodology): “…such as difficulty of identification model’s parameters and not easily capturing nonlinear characteristics of runoff time series (Kong et al. 2015; Yaseen et al. 2015), artificial neural network (ANN), adaptive network-based fuzzy inference system (ANFIS) and support vector machine (SVM) are…”
Survey of computational intelligence as basis to big flood management: challenges, research directions and future work (2018, methodology): “They can also be used in optimization of the weights between an ANN’s neurons or with the combination of other algorithms (e.g. PSO, fuzzy logic) to decrease the error in the prediction of load demand (Tahmasebi & Hezarkhani, 2012).”
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, background): “The concise outline here cannot possibly do justice, and readers are referred to multiple review papers for more details (e.g., Dawson & Wilby, 2001; Fallah-Mehdipour et al., 2013; Jha & Sahoo, 2015; Raghavendra & Deka, 2014; Tsai et al., 2017; W.-C. Wang et al., 2009; Yaseen et al., 2015).”