Multiple Random Forests Modelling for Urban Water Consumption Forecasting

Chen, Guoqiang; Long, Tianyu; Xiong, Jiangong; Bai, Yun

doi:10.1007/s11269-017-1774-7

Cited by 79 publications

(39 citation statements)

References 27 publications

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“…The summary of tree-based data mining algorithms is presented in Table 1. These tree-based algorithms are frequently used in many fields and for different applications (Kim et al 2011;Rodriguez-Galiano et al 2012;Rahmati et al 2016;Yoo et al 2016;Belgiu and Drăguţ 2016;Hong et al 2016;Naghibi et al 2017;Heil et al 2017;Chen et al 2017;Robinson et al 2018;Rayaroth and Sivaradje 2019;Al-Juboori 2019). For specific detail on the theoretical bases of data mining, its algorithms, and application, the reader is referred to the works of Rokach and Maimon (2005), Han et al (2011), Liao et al (2012), Rokach and Maimon (2014).…”

Section: Introductionmentioning

confidence: 99%

Decision Tree-Based Data Mining and Rule Induction for Identifying High Quality Groundwater Zones to Water Supply Management: a Novel Hybrid Use of Data Mining and GIS

Jeihouni

Toomanian

Mansourian

2019

Water Resour Manage

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Groundwater is an important source to supply drinking water demands in both arid and semiarid regions. Nevertheless, locating high quality drinking water is a major challenge in such areas. Against this background, this study proceeds to utilize and compare five decision treebased data mining algorithms including Ordinary Decision Tree (ODT), Random Forest (RF), Random Tree (RT), Chi-square Automatic Interaction Detector (CHAID), and Iterative Dichotomiser 3 (ID3) for rule induction in order to identify high quality groundwater zones for drinking purposes. The proposed methodology works by initially extracting key relevant variables affecting water quality (electrical conductivity, pH, hardness and chloride) out of a total of eight existing parameters, and using them as inputs for the rule induction process. The algorithms were evaluated with reference to both continuous and discrete datasets. The findings were speculative of the superiority, performance-wise, of rule induction using the continuous dataset as opposed to the discrete dataset. Based on validation results, in continuous dataset, RF and ODT showed higher and RT showed acceptable performance. The groundwater quality maps were generated by combining the effective parameters distribution maps using inducted rules from RF, ODT, and RT, in GIS environment. A quick glance at the generated maps reveals a drop in the quality of groundwater from south to north as well as from east to west in the study area. The RF showed the highest performance (accuracy of 97.10%) among its counterparts; and so the generated map based on rules inducted from RF is more reliable. The RF and ODT methods are more suitable in the case of continuous dataset and can be applied for rule induction to determine water quality with higher accuracy compared to other tested algorithms.

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Section: Introductionmentioning

confidence: 99%

Decision Tree-Based Data Mining and Rule Induction for Identifying High Quality Groundwater Zones to Water Supply Management: a Novel Hybrid Use of Data Mining and GIS

Jeihouni

Toomanian

Mansourian

2019

Water Resour Manage

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“…Precise short-term prediction of urban water demand provides guidance for the planning and management of water resources and plays an important role in the economic operation of a water supply system. Therefore, various water demand prediction models, such as support vector regression (SVR) [1,2], random forests regression [3], artificial neural network (ANN) [4], Markov chain model [5], and hybrid models [6][7][8][9], have been widely developed in the past few decades. Research regarding water demand prediction generally focuses on methods involving ANN, which are nonparametric data-driven approaches applicable for building nonlinear mapping from input to output variables for estimating nonlinear continuous functions with an arbitrary accuracy [10].…”

Section: Introductionmentioning

confidence: 99%

Hourly Urban Water Demand Forecasting Using the Continuous Deep Belief Echo State Network

Zhang

Long

et al. 2019

Water

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Effective and accurate water demand prediction is an important part of the optimal scheduling of a city water supply system. A novel deep architecture model called the continuous deep belief echo state network (CDBESN) is proposed in this study for the prediction of hourly urban water demand. The CDBESN model uses a continuous deep belief network (CDBN) as the feature extraction algorithm and an echo state network (ESN) as the regression algorithm. The new architecture can model actual water demand data with fast convergence and global optimization ability. The prediction capacity of the CDBESN model is tested using historical hourly water demand data obtained from an urban waterworks in Zhuzhou, China. The performance of the proposed model is compared with those of ESN, continuous deep belief neural network, and support vector regression models. The correlation coefficient (r2), normalized root-mean-square error (NRMSE), and mean absolute percentage error (MAPE) are adopted as assessment criteria. Forecasting results obtained in the testing stage indicate that the CDBESN model has the largest r2 value of 0.995912 and the smallest NRMSE and MAPE values of 0.027163 and 2.469419, respectively. The prediction accuracy of the proposed model clearly outperforms those of the models it is compared with due to the good feature extraction ability of CDBN and the excellent feature learning ability of ESN.

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“…At each split of a tree within a forest, a test is performed by selecting a random subset of the independent variables [2]. The explanatory variables that are used as input to the model represent the roots and the output is the leaves [1]. The number of trees to grow and the number of the independent variables to be randomly selected at each node are defined by the user.…”

Section: Random Forestsmentioning

confidence: 99%

“…Predictions of urban water consumption are essential in water economies, especially under the threat of unprecedented water shortages [1]. Short-term water demand forecasting provides estimates of demand over the next hours or weeks to make informed operational, tactical, and strategic decisions that will improve the performance of the network [4,10].…”

Section: Introductionmentioning

confidence: 99%

“…A variety of studies have explored the application of machine learning methods such as Artificial Neural Networks, Support Vector Machines, and fuzzy logic in short-term water demand forecasting [6,7,8,12,18], or even hybrid models that combine one or more methods, but Random Forests have rarely been used in the water demand literature. The few studies that attempted this [1,2,10], although limited by data availability, demonstrated the potential of this method to accurately capture the dynamic nature of water consumption.…”

Section: Introductionmentioning

confidence: 99%

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Smart Water Demand Forecasting: Learning from the Data

Xenochristou

Kapelan

Hutton³

et al.

EPiC Series in Engineering

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Accurate forecasts of demand are essential for water utilities in order to manage, plan, and optimize the operation of their network. This work aims to develop a new method for short- term water demand forecasting by utilizing a new data-driven approach based on Random Forests, as well as consumption recordings, household, and socio-economic characteristics, and weather data. Initial results, obtained on real-life consumption data from the UK, demonstrate the potential of this method and show the importance of disaggregating consumption when attempting to determine the influence of weather on water demand. In this study, adding weather input to the model achieved improved forecasting accuracy, especially for the aggregation of properties with medium occupancy and affluent residents during summer months.

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Multiple Random Forests Modelling for Urban Water Consumption Forecasting

Cited by 79 publications

References 27 publications

Decision Tree-Based Data Mining and Rule Induction for Identifying High Quality Groundwater Zones to Water Supply Management: a Novel Hybrid Use of Data Mining and GIS

Decision Tree-Based Data Mining and Rule Induction for Identifying High Quality Groundwater Zones to Water Supply Management: a Novel Hybrid Use of Data Mining and GIS

Hourly Urban Water Demand Forecasting Using the Continuous Deep Belief Echo State Network

Smart Water Demand Forecasting: Learning from the Data

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