Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand…

By Jan Adamowski, Hiu Fung Chan, Shiv O. Prasher, B. Ozga-Zieliński, A. Sliusarieva. 2012

460 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/c9d766e1555a7d6b8ae61781a00643c58be0bd83

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In Environmental Engineering

In Global and Planetary Change

In Ocean Engineering

Citations in the corpus

Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models (2016, methodology): “…diverse terrains, the parameters of such models need calibration based on field observations and further fine-tuning is required to downscale the outputs of Global Climate Models to local-scales processes especially in practical decision-support (Adamowski et al. 2012; Goyal et al. 2014).”
Bootstrap rank‐ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling (2016, result): “Based on previous studies by one of the contributing authors [Adamowski, 2008; Adamowski and Karapataki, 2010; Adamowski et al., 2012; Tiwari and Adamowski, 2013; Adamowski et al., 2013; Tiwari and Adamowski, 2015] previous days’ UWD, rainfall (rain (mm/day)), and maximum air temperature (mat (8C))…”
Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models (2014, background): “…river flow (Adamowski, 2008; Adamowski and Sun, 2010), to forecast groundwater levels (Adamowski and Chan, 2011), to forecast urban water demand (Adamowski et al., 2012) and for the purposes of drought forecasting (Kim and Valdes, 2003; Ozger et al., 2012; Mishra and Singh, 2012; Belayneh and…”
Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey (2018, methodology): “…important to note that for both MLP-based predictive models, the Levenberg–Marquardt (LM) backpropagation learning algorithm considered as a fast and efficient tool compared to the other training algorithm (e.g., Adamowski et al. 2012; Deo and Şahin 2015b, 2016; Deo et al. 2017b) has been adopted.”
Probabilistic Prediction for Monthly Streamflow through Coupling Stepwise Cluster Analysis and Quantile Regression Methods (2016, methodology): “These approaches primarily include statistical regression (Sadri and Burn 2012; Adamowski and Karapataki 2008; Adamowski et al. 2012; Sachindra et al. 2013), artificial intelligence (Cigizoglu 2005; Turan and Yurdusev 2009; Wang et al. 2009; Yang et al. 2015) and machine learning methods (Okkan and…”
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, methodology): “…were useful for assessment in various related areas of river discharge, streamflow, drought and temperature modelling (Tiwari and Chatterjee 2011; Adamowski et al. 2012; Deo and Şahin 2015a, b; Salcedo-Sanz et al. 2015). r2 describes the proportion of the total statistical variance in…”
Flood Prediction Using Machine Learning Models: Literature Review (2018, background): “ELM for ﬂood prediction recently became of interest for hydrologists and was used to model short-term streamﬂow with promising results [93,94].”
A stepwise-cluster forecasting approach for monthly streamflows based on climate teleconnections (2015, methodology): “…autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), and multiple linear regression (MLR) approaches have been widely used in hydrological predictions (Wu 2010; Sadri and Burn 2012; Adamowski and Karapataki 2008; Adamowski et al. 2012).”
Drought forecasting through statistical models using standardised precipitation index: a systematic review and meta-regression analysis (2019, background): “An antecedent step in neural network models is to normalise the data before using it for training (Belayneh and Adamowski 2012).”
Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, methodology): “…ANN DWT Evapotranspiration Daily Shankar et al. (2011) Fuzzy DWT Land cover – Abghari et al. (2012) ANN Active function Evapotranspiration Daily Adamowski et al. (2012) ANN DWT Urban water demand, precipitation, temperature Daily Kisi (2009b) ANN DWT Lake level Monthly Campisi et al. (2012)…”
A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework (2018, background): “If the parameter is too small or too large, an under-fitting or over-fitting phenomenon can occur (Wu et al. 2009; Adamowski et al. 2012; Huang et al. 2014; Mahmoodi et al. 2014), which can increase the training error and decrease the generalization ability.”