Time series modelling of water resources and environmental systems

By Keith W. Hipel, Ian McLeod. 1994

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Citations in the corpus

Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, methodology): “It was taken from a book by Hipel and McLeod (1994) and can be downloaded from http://www.”
A comparison of the power of the t test, Mann-Kendall and bootstrap tests for trend detection / Une comparaison de la puissance des tests t de Student, de Mann-Kendall et du bootstrap pour la détection de tendance (2004, methodology): “…trends in hydro- Sheng Yue & Paul Pilon 22 meteorological time series (e.g. ven Belle & Hughes, 1984; Cailas et al., 1986; Hipel et al., 1988; Hipel & McLeod, 1994; Taylor & Loftis, 1989; Demarée & Nicolis, 1990; Zetterqvist, 1991; Chiew & McMahon, 1993; Yu et al., 1993; Hirsch et al.,…”
Stochastic simulation of nonstationary oscillation hydroclimatic processes using empirical mode decomposition (2012, methodology): “A nonlinear classical ARX model [Nelles, 2001] was also tested as a surrogate of the ARX model.”
Nesting biology and conservation of the olive ridley sea turtle (Lepidochelys olivacea) in Brazil, 1991/1992 to 2002/2003 (2007, methodology): “A test for trend on the number of nests per season was performed by means of the nonparametric nonseasonal Mann–Kendall test (Hipel & McLeod, 1994).”
Trend analysis of water quality series based on regression models with correlated errors (2011, methodology): “A wide review of time70 series models can be found in Hipel and McLeod (1994) and numerous refer-71 ences exist in WQ literature, such as Bhangu and Whitfield (1997), Worrall72 and Burt (1999), Ahmad et al. (2001) and Lehmann and Rode (2001), for73 example.”
Artificial neural network modeling of water table depth fluctuations (2001, methodology): “Therefore a common approach use empirical time series models such as described by Box Jenkins [1976] and Hipel and McLeod [1994] to generate a time series of water table depths.”
A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series (2009, methodology): “ARMA is also applied to monthly hydrologic time series (Hipel and Mcleod, 1994).”
Time series forecasting using a hybrid ARIMA and neural network model (2003, methodology): “This model has also been used by many researchers such as Subba Rao and Gabr [33] and Hipel and McLeod [16].”
Time series interpolation via global optimization of moments fitting (2013, background): “A more detailed description of the series can be found at [21].”
Testing the Residuals of an ARIMA Model on the Çekerek Stream Watershed in Turkey (2005, methodology): “The existence or lack of stationarity in a time series can be detected by alternative methods, i.e. nonparametric tests (including Kendall’s tau, Mann-Kendall and Sen tests), a unit root test and a graphical approach (Hipel and McLeod, 1994; Gibbons, 1997; Greene, 2000).”
pyMannKendall: a python package for non parametric Mann Kendall family of trend tests (2019, methodology): “Seasonal sen’s Slope Estimator ( seasonal_sens_slope ): This method was proposed by Hipel & McLeod (1994) to estimate the magnitude of the monotonic trend, when data has seasonal effects. pyMannKendall is a non-parametric Mann-Kendall trend analysis package implemented in pure Python, which brings…”
Estimation of nonlinear trends in water quality: An improved approach using generalized additive models (2008, background): “Hipel and McLeod [1994] give a very full account of time series applications to water resources, but do not include semiparametric models.”