Predictability of monthly temperature and precipitation using automatic time series forecasting methods

Papacharalampous, Georgia; Koutsoyiannis, Demetris

doi:10.1007/s11600-018-0120-7

Cited by 108 publications

(64 citation statements)

References 43 publications

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“…To begin, ARIMA does not use information from exogenous variables, which leads to a limit of predictability. Including relevant data as model input, such as precipitation and snowmelt, could sometimes help address this problem [39]. When weather forecasts are used as model input for wastewater influent forecasting, it is important to ensure the accuracy/quality of the weather forecasts.…”

Section: Discussionmentioning

confidence: 99%

Influent Forecasting for Wastewater Treatment Plants in North America

Boyd

et al. 2019

Sustainability

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Autoregressive Integrated Moving Average (ARIMA) is a time series analysis model that can be dated back to 1955. It has been used in many different fields of study to analyze time series and forecast future data points; however, it has not been widely used to forecast daily wastewater influent flow. The objective of this study is to explore the possibility for wastewater treatment plants (WWTPs) to utilize ARIMA for daily influent flow forecasting. To pursue the objective confidently, five stations across North America are used to validate ARIMA’s performance. These stations include Woodward, Niagara, North Davis, and two confidential plants. The results demonstrate that ARIMA models can produce satisfactory daily influent flow forecasts. Considering the results of this study, ARIMA models could provide the operating engineers at both municipal and rural WWTPs with sufficient information to run the stations efficiently and thus, support wastewater management and planning at various levels within a watershed.

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Section: Discussionmentioning

confidence: 99%

Influent Forecasting for Wastewater Treatment Plants in North America

Boyd

et al. 2019

Sustainability

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“…Concerning this method, there is a new flexible additive model developed by Facebook (Prophet) that considers non-periodic changes in trends as well as customizable seasonal periodic components in a Bayesian framework as easily interpretable parameters (Taylor and Letham 2018a). This model's applicability to hydrometeorological data has already been highlighted by some authors such as Papacharalampous et al (2018).…”

Section: Ngwaorgmentioning

confidence: 99%

Unraveling the Hydrological Behavior of a Coastal Pond in Doñana National Park (Southwest Spain)

Fernández-Ayuso¹,

Aguilera²,

Guardiola‐Albert³

et al. 2019

Groundwater

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Time series analysis methods have been used to detect behavioral patterns in a set of nine time series. These series contained information in a 3‐h time step about meteorological, hydrological and tidal data of a sand dune pond area located in Doñana National Park in the southwest of Spain. The methods used, such as wavelet analysis and additive seasonal decomposition, had never been applied before in the types of ecosystems studied. These approaches have improved the current knowledge of the conceptual model of the Santa Olalla pond system, the only system with a permanent hydroperiod located in this protected area. In addition, complex surface water‐groundwater interactions, not visible through descriptive methods, have been distinguished to have a strong seasonal component. Finally, we evaluated the effect of pumping activity in a nearby coastal resort on the water supply of the Santa Olalla pond system. Although direct damage to this sand dune pond has not yet been identified, special attention must be paid in order to maintain groundwater inputs that are integral to maintaining its current status.

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“…Calculate the DSE value: DSE is obtained by computing Shannon entropy from the probability distribution for all the words, as shown in equation (11). Its normalized form is…”

Section: Differential Symbolic Entropymentioning

confidence: 99%

“…Murty et al [10] used SARIMA to predict the monthly mean maximum and minimum temperatures in India. Papacharalampous et al [11] used the ARFIMA model to apply the automatic univariate time series prediction method to predict the monthly mean temperature and precipitation. e statistical model is easy to use and has a small amount of calculation.…”

Section: Introductionmentioning

confidence: 99%

Monthly Mean Meteorological Temperature Prediction Based on VMD-DSE and Volterra Adaptive Model

Chang

Yang

2020

Advances in Meteorology

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Climate is a complex and chaotic system, and temperature prediction is a challenging problem. Accurate temperature prediction is also concerned in the fields of energy, environment, industry, and agriculture. In order to improve the accuracy of monthly mean temperature prediction and reduce the calculation scale of hybrid prediction process, a combined prediction model based on variational mode decomposition-differential symbolic entropy (VMD-DSE) and Volterra is proposed. Firstly, the original monthly mean meteorological temperature sequence is decomposed into finite mode components by VMD. The DSE is used to analyze the complexity and reconstruct the sequences. Then, the new sequence is reconstructed in phase space. The delay time and embedding dimension are determined by the mutual information method and G-P method, respectively. On this basis, the Volterra adaptive prediction model is established to modeling and predicting each component. Finally, the final predicted values are obtained by superimposing the predicted results. The monthly mean temperature data of Xianyang and Yan’an are used to verify the prediction performance of the proposed model. The experimental results show that the VMD-DSE-Volterra model shows better performance in the prediction of monthly mean temperature compared with other benchmark models in this paper. In addition, the combined forecasting model proposed in this paper can reduce the modeling time and improve the forecasting accuracy, so it is an effective forecasting model.

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Predictability of monthly temperature and precipitation using automatic time series forecasting methods

Cited by 108 publications

References 43 publications

Influent Forecasting for Wastewater Treatment Plants in North America

Influent Forecasting for Wastewater Treatment Plants in North America

Unraveling the Hydrological Behavior of a Coastal Pond in Doñana National Park (Southwest Spain)

Monthly Mean Meteorological Temperature Prediction Based on VMD-DSE and Volterra Adaptive Model

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