Forecasting monthly urban water demand using Extended Kalman Filter and Genetic Programming

Nasseri, Mohsen; Moeini, Ali; Tabesh, Massoud

doi:10.1016/j.eswa.2010.12.087

Cited by 94 publications

(48 citation statements)

References 27 publications

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“…The Kalman filter is an algorithm that operates recursively on streams of noisy input data to produce a statistically optimal estimate of the underlying system state. They have been applied in many areas, including navigation (Yim et al, 2011), water demand prediction (Nasseri et al, 2011), and traffic volume forecasting (Xie et al, 2007). An introduction to Kalman filter theory is given by Haykin (2001).…”

Section: Kalman Filtermentioning

confidence: 99%

Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter

Jin

Wang

Cheng

et al. 2013

J. Zhejiang Univ. Sci. A

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Abstract:In this paper, a prediction model is developed that combines a Gaussian mixture model (GMM) and a Kalman filter for online forecasting of traffic safety on expressways. Raw time-to-collision (TTC) samples are divided into two categories: those representing vehicles in risky situations and those in safe situations. Then, the GMM is used to model the bimodal distribution of the TTC samples, and the maximum likelihood (ML) estimation parameters of the TTC distribution are obtained using the expectation-maximization (EM) algorithm. We propose a new traffic safety indicator, named the proportion of exposure to traffic conflicts (PETTC), for assessing the risk and predicting the safety of expressway traffic. A Kalman filter is applied to forecast the short-term safety indicator, PETTC, and solves the online safety prediction problem. A dataset collected from four different expressway locations is used for performance estimation. The test results demonstrate the precision and robustness of the prediction model under different traffic conditions and using different datasets. These results could help decision-makers to improve their online traffic safety forecasting and enable the optimal operation of expressway traffic management systems.

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Section: Kalman Filtermentioning

confidence: 99%

Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter

Jin

Wang

Cheng

et al. 2013

J. Zhejiang Univ. Sci. A

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“…Sen et al (2009) established a fuzzy model for predicting daily drinking water requirement for a person [2] ; Firat et al (2010) found CCNN model performed better than GRNN model and FFNN model by comparing the prediction effect of daily water demand in Izmir, Turkey [3] . Herrera et al (2010) made water demand prediction of a city in southeastern Spain, the results indicated SVM model had the highest prediction accuracy, followed by multivariate adaptive regression spline model, projection pursuit model, random forest model and neural network model [4] ; Nasseri et al (2011) established a genetic algorithm model to predict urban water demand in Tehran [5] ; Ajbar et al (2013) built a neural network model to forecast the monthly and annual water demand for Mecca city, Saudi Arabia [6] . These forecasting methods mainly are neural network model (ANNs), fuzzy systems theory model, projection pursuit model and genetic algorithms, or improved model and combined model.…”

Section: Introductionmentioning

confidence: 99%

“…These forecasting methods mainly are neural network model (ANNs), fuzzy systems theory model, projection pursuit model and genetic algorithms, or improved model and combined model. However, these models generally do not have high prediction accuracy, whose errors are usually higher than 5%, and are not conducive to analyze how the factors affect the water demand [2][3][4][5][6] .…”

Section: Introductionmentioning

confidence: 99%

Forecasting on China’s Total Water Demand in 2018

Liu

2018

Advances in Intelligent Systems Research

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Abstract-To forecast total water demand in advance is practically important for water supply planning. The paper first made impacting factors analysis of the total water demand in China. Then established three models for the total water demand forecasting by multiple regression analysis. The fitting precision of the forecasting models is satisfactory. Applied the models and expert experiences, made the total water demand forecast in China 2018 in some scenarios. The results show that the total water demand will be 608.04 billion m 3 in 2018 in China.

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“…Therefore, considering those factors could be a constraint for the implementation. This way, Nasseri et al (2011) developed a model based on AI techniques (genetic algorithms and Kalman filter) with excellent results, taking only in consideration data from previous demand.…”

Section: Introductionmentioning

confidence: 99%

Real-time water demand forecasting system through an agent-based architecture

Ponte

García

Pino

et al. 2015

IJBIC

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Water policies have evolved enormously since the Rio Earth Summit (1992). These changes have led to the strategic importance of Water Demand Management. The aim is to provide water where and when it is required using the fewest resources. A key variable in this process is the demand forecasting. It is not sufficient to have long term forecasts, as the current context requires the continuous availability of reliable hourly predictions. This paper incorporates artificial intelligence to the subject, through an agent-based system, whose basis are complex forecasting methods (BoxJenkins, Holt-Winters, Multi-Layer Perceptron Networks and Radial Basis Function Networks). The prediction system also includes data mining, oriented to the pre and post processing of data and to the knowledge discovery, and other agents. Thereby, the system is capable of choosing at every moment the most appropriate forecast, reaching very low errors. It significantly improves the results of the different methods separately.

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Forecasting monthly urban water demand using Extended Kalman Filter and Genetic Programming

Cited by 94 publications

References 27 publications

Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter

Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter

Forecasting on China’s Total Water Demand in 2018

Real-time water demand forecasting system through an agent-based architecture

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