Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution

Kisi, Özgür; Parmar, Kulwinder Singh

doi:10.1016/j.jhydrol.2015.12.014

Cited by 299 publications

(116 citation statements)

References 42 publications

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“…This method is a nonparametric regression initially proposed by Friedman (1991). It is used to study the nonlinear relation between a response variable and a set of predictor variables, and has been successfully applied in several areas of knowledge (GARCÍA NIETO; ÁLVAREZ ANTÓN, 2014; KOC; BOZDOGAN, 2015;KISI;PARMAR, 2016;DEO et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Reference evapotranspiration estimated from air temperature using the MARS regression technique

Ferreira¹,

Duarte²,

Araújo³

et al. 2018

Biosci. J.

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ABSTRACT:The reference evapotranspiration (ET o ) is an important component for determining the water requirements of the crops. In order to estimate this variable accurately, the Food and Agriculture Organization (FAO) proposed the Penman-Monteith equation, however, this demands a large number of meteorological data, which restricts its use. In this context, this study compares the performance of the Penman-Monteith equation using only measured air temperature (PMT) and the Hargreaves-Samani (HS) equation with the performance of the multivariate adaptive regression splines (MARS) technique for the daily ET o estimation with only air temperature data. For the study, daily meteorological data from 2002 to 2016 were used. The data were collected from weather stations located in Florianópolis-SC, Manaus-AM and Petrolina-PE, being these selected in order to capture different climatic conditions. MARS models were developed for each weather station and the PMT e HS equations were locally calibrated. The performances of the original and calibrated equations and MARS models were evaluated based on the statistical indices root mean square error, mean absolute error, mean bias error and coefficient of determination. The ET o estimated by the Penman-Monteith method with full data was used as reference for the development of the MARS models, calibration of the equations and for the performance evaluation of the models under study. The calibration of the HS and PMT equations promoted better performances in relation to the original equations, improving the methods accuracy. The MARS technique presented good performance, outperforming the original and calibrated PMT and HS equations, with lower error values and higher coefficient of determination, and can be considered as an alternative to empirical methods.

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Section: Introductionmentioning

confidence: 99%

Reference evapotranspiration estimated from air temperature using the MARS regression technique

Ferreira¹,

Duarte²,

Araújo³

et al. 2018

Biosci. J.

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“…Zhiyong Liu and others investigated a hybrid model that was combined the discrete wavelet transform and support vector regression (the DWT-SVR model) for daily and monthly stream flow forecasting and found it outperformed regular SVR [6]. In addition, some other hybrid models and unicity models for forecasting have also been proposed, such as AGA-SSVR which hybridizes SVR model with adaptive genetic algorithm (AGA) and the seasonal index adjustment [7], Recurrent Neural Networks(RNN) and Grammatical Inference [8], the least squares support vector regression [9,10], SVR or SVM [11,12,13,14], Neural Networks [15]. Most of the above are only developed to short-term time series prediction.…”

Section: Introductionmentioning

confidence: 99%

“…Multiple-output support vector regression (M-SVR) have been employed in multi-step-ahead time series prediction by Yukun Bao and others [17]. The least squares support vector machine and the multivariate adaptive regression spline model are applied to the long-term prediction of river water pollution by Ozgur Kisi et al [10].…”

Section: Introductionmentioning

confidence: 99%

Electricity Consumption Prediction Using XGBoost Based on Discrete Wavelet Transform

Wang¹,

Shi²,

Lyu³

et al. 2017

dtcse

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The purpose of this paper is to predict the daily electricity consumption of the next month. It is considerably important for people to cope with the problem well. Although few articles mentions the topic of electricity consumption prediction, numerous papers include some topic similar to the topic in this paper, such as rainfall forecasting, wind speed prediction and water flow forecasting. Moreover, a number of techniques and algorithms are employed to cope with those issues and achieve outstanding performance. Those techniques and algorithms are considerably remarkable, but the accuracy of them is not excellent enough on the long-term prediction of time series. In this paper, we propose a hybrid model which integrate discrete wavelet transform and XGBoost to forecast the electricity consumption time series data on the long-term prediction, namely DWT-XGBoost. The original time series data can decompose into approximate time series data and detail time series data by the discrete wavelet transform. And those time series data by decomposition are as features input into the prediction model that is XGBoost. Furthermore, the parameters of XGBoost are obtained by a grid search method. The performance of the proposed model in this paper is measured against with other hybrid models such as integrating discrete wavelet transform and support vector regression, integrating discrete wavelet transform and artificial neural networks, and unitary XGBoost. The comparison results show that the DWT-XGBoost outperforms other models and is a novel method on the long-term prediction of time series.

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“…Chang et al, 2013;Duku et al, 2015), raw-water quality (Zhang and Stanley, 1997) and a range of other water quality parameters (e.g. Pulido-Velazquez et al, 2015;Kisi and Parmar, 2016), such as suspended sediment (e.g. Mount et al, 2012;Duan et al, 2015), phosphate (e.g.…”

Section: Introduction 35mentioning

confidence: 99%

Modelling salinity in river systems using hybrid process and data-driven models

Hunter

Maier

Gibbs

et al. 2017

Preprint

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Abstract. Salinity modelling in river systems is complicated by a number of processes, including in-stream salt transport and various mechanisms of saline accession that vary dynamically as a function of water level and flow, often at different temporal scales. Traditionally, salinity models in rivers have either been process-or data-driven. The primary problem with process-10 based models is that in many instances, not all of the underlying processes are fully understood or able to be represented mathematically, and that there are often insufficient historical data to support model development. The major limitation of data-driven models, such as artificial neural networks (ANNs), is that they provide limited system understanding and are generally not able to be used to inform management decisions targeting specific processes, as different processes are generally modelled implicitly. In order to overcome these limitations, a hybrid modelling approach is introduced and applied in this 15paper. As part of the approach, the most suitable sub-models are developed for each sub-process affecting salinity at the location of interest based on consideration of model purpose, degree of process understanding and data availability, which are then combined to form the hybrid model. The approach is applied to a 46 km reach of the River Murray in South Australia, which is affected by high levels of salinity. In this reach, the major processes affecting salinity include in-stream salt transport, Hydrol. Earth Syst. Sci. Discuss., https://doi

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Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution

Cited by 299 publications

References 42 publications

Reference evapotranspiration estimated from air temperature using the MARS regression technique

Reference evapotranspiration estimated from air temperature using the MARS regression technique

Electricity Consumption Prediction Using XGBoost Based on Discrete Wavelet Transform

Modelling salinity in river systems using hybrid process and data-driven models

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