A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation

By Vahid Nourani, Mohammad Taghi Aalami, Mohammad H. Aminfar. Engineering applications of artificial intelligence, 2009

319 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/5972dee596c86270442ed4f1cf838c10d0c8eaff

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jan Adamowski
2. Kwok-Wing Chau
3. Vahid Nourani
4. A. H. Baghanam
5. Xiaohu Wen
6. Qi Feng
7. Jianguo Li
8. K. Hasan

In Electrical and Electronic Engineering

1. Kwok-Wing Chau
2. Jan Adamowski
3. Xiaohu Wen
4. Qi Feng
5. Amir Mosavi
6. Jianguo Li
7. Vahid Nourani
8. A. H. Baghanam

In Environmental Engineering

1. Jan Adamowski
2. Kwok-Wing Chau
3. Ozgur Kisi
4. Vahid Nourani
5. A. H. Baghanam
6. Xiaohu Wen
7. Amir Mosavi
8. Qi Feng

Citations in the corpus

• Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression (2021, background): “This happens because WT decomposes complex time series by catching information on different levels, which results in a more effective structure for machine learning methods (Adamowski & Sun, 2010; Nourani et al., 2009; Zhou et al., 2020).”
• Wavelet Analysis-Support Vector Machine Coupled Models for Monthly Rainfall Forecasting in Arid Regions (2015, methodology): “Nourani et al. (2009) developed a WA-ANN coupled model for 1-month ahead forecasting of Ligvanchai watershed precipitation at Tabriz, Iran.”
• Predicting Water Level Fluctuations in Lake Michigan-Huron Using Wavelet-Expert System Methods (2014, background): “Many applications on wavelet combination models have been studied in the literature (Kim and Valdes 2003; Kucuk et al. 2009; Webster and Hoyos 2004; Nourani et al. 2009; Ozger 2010; Shiri and Kisi 2010; Shiri et al. 2011).”
• Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh (2017, methodology): “Recently, the combined wavelet-artificial neural network (Wavelet-ANN) model has been widely used to forecast hydrological and hydrogeological phenomena (Solgi et al., 2014; Okkan, 2012a,b; Nourani et al., 2009).”
• A hybrid support vector regression–firefly model for monthly rainfall forecasting (2018, methodology): “Accordingly, application of hybrid AI models such as wavelet–ANN (Nourani et al. 2009), wavelet–SVM (Kisi and Cimen 2012), and adaptive network-based fuzzy inference system (Mekanik Editorial responsibility: M.”
• Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, methodology): “…minimum level of decomposition, L, to the number of data points within the time series Ns, was introduced in the literature (Aussem et al., 1998; Nourani et al., 2009b; Wang and Ding, 2003): L ¼ int½log Ns ð1Þ Later, Nourani et al. (2011) criticized the outcome of this formula, stating that,…”