Stream Flow Forecasting of Poorly Gauged Mountainous Watershed by Least Square Support Vector Machine, Fuzzy Genetic Algorithm and M5 Model Tree Using Climatic Data from Nearby Station

Adnan, Rana Muhammad; Yuan, Xiaohui; Kisi, Özgür; Adnan, Muhammad; Mehmood, Asif

doi:10.1007/s11269-018-2033-2

Cited by 48 publications

(15 citation statements)

References 29 publications

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“…There are a few studies which showed that the M5RT could achieve proper estimation accuracy but requires extensive sampling input data [ 19 ]. In general, M5RT is considered a straightforward procedure that could be worthwhile for estimation applications, especially with the availability of quite large amounts of data [ 17 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Reference Evapotranspiration Modeling Using New Heuristic Methods

Chen

Yuan

Kisi

et al. 2020

Entropy

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The study investigates the potential of two new machine learning methods, least-square support vector regression with a gravitational search algorithm (LSSVR-GSA) and the dynamic evolving neural-fuzzy inference system (DENFIS), for modeling reference evapotranspiration (ETo) using limited data. The results of the new methods are compared with the M5 model tree (M5RT) approach. Previous values of temperature data and extraterrestrial radiation information obtained from three stations, in China, are used as inputs to the models. The estimation exactness of the models is measured by three statistics: root mean square error, mean absolute error, and determination coefficient. According to the results, the temperature or extraterrestrial radiation-based LSSVR-GSA models perform superiorly to the DENFIS and M5RT models in terms of estimating monthly ETo. However, in some cases, a slight difference was found between the LSSVR-GSA and DENFIS methods. The results indicate that better prediction accuracy may be obtained using only extraterrestrial radiation information for all three methods. The prediction accuracy of the models is not generally improved by including periodicity information in the inputs. Using optimum air temperature and extraterrestrial radiation inputs together generally does not increase the accuracy of the applied methods in the estimation of monthly ETo.

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Section: Introductionmentioning

confidence: 99%

Reference Evapotranspiration Modeling Using New Heuristic Methods

Chen

Yuan

Kisi

et al. 2020

Entropy

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“…Distinct input lags were used as inputs to the models and results were evaluated using root mean square error (RMSE), mean absolute error (MAE) and determination coefficient (R 2 ). These indexes have been used extensively in literature [33][34][35][36]. The RMSE and MAE statistics are expressed ( 6) till ( 7):…”

Section: Case Study and Performance Indicatorsmentioning

confidence: 99%

Air temperature prediction using different machine learning models

Adnan

Liang

Kuriqi

et al. 2021

IJEECS

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Air temperature is an essential climatic component particularly in water resources management and other agro-hydrological/meteorological activities planning This paper examines the prediction capability of three machine learning models, least square support vector machine (LSSVM), group method and data handling neural network (GMDHNN) and classification and regression trees (CART) in air temperature forecasting using monthly temperature data of Astore and Gilgit climatic stations of Pakistan. The prediction capability of three machine learning models is evaluated using different time lags input combinations with help of root mean square error (RMSE), the mean absolute error (MAE) and coefficient of determination (R<sup>2</sup>).statistical indicators. The obtained results indicated that the LSSVM model is more accurate in temperature forecasting than GMDHNN and CART models. LSSVM significantly decreases the mean RMSE of the GMHNN and CART models by 1.47-3.12% and 20.01-25.12% for the Chakdara and Kalam Stations, respectively.

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“…In most of these researches, the consequences of the river streamflow and the suspended sediment load have been used and made available as input while the future value of the sediment load is considered as the desired output. Different pre-processing analyses have been carried out before implementing the model such as normalization, sensitivity analysis and statistical analysis including the correlation and cross correlation to select the most effective pattern of the input variables to achieve the best model performance [19]. In addition, comprehensive analysis has been carried out for the selection of the best training algorithms including gradient decent (GD), conjugate gradient (CG) and Levenberg-Marquardt (LM) [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Suspended Sediment Load Using Data-Driven Models

Adnan

Liang

El-Shafie

et al. 2019

Water

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Estimation of suspended sediments carried by natural rivers is essential for projects related to water resource planning and management. This study proposes a dynamic evolving neural fuzzy inference system (DENFIS) as an alternative tool to estimate the suspended sediment load based on previous values of streamflow and sediment. Several input scenarios of daily streamflow and suspended sediment load measured at two locations of China-Guangyuan and Beibei-were tried to assess the ability of this new method and its results were compared with those of the other two common methods, adaptive neural fuzzy inference system with fuzzy c-means clustering (ANFIS-FCM) and multivariate adaptive regression splines (MARS) based on three commonly utilized statistical indices, root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE). The data period covers 01/04/2007-12/31/2015 for the both stations. A comparison of the methods indicated that the DENFIS-based models improved the accuracy of the ANFIS-FCM and MARS-based models with respect to RMSE by 33% (32%) and 31% (36%) for the Guangyuan (Beibei) station, respectively. The NSE accuracy for ANFIS-FCM and MARS-based models were increased by 4% (36%) and 15% (19%) using DENFIS for the Guangyuan (Beibei) station, respectively. It was found that the suspended sediment load can be accurately estimated by DENFIS-based models using only previous streamflow data.

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Stream Flow Forecasting of Poorly Gauged Mountainous Watershed by Least Square Support Vector Machine, Fuzzy Genetic Algorithm and M5 Model Tree Using Climatic Data from Nearby Station

Cited by 48 publications

References 29 publications

Reference Evapotranspiration Modeling Using New Heuristic Methods

Reference Evapotranspiration Modeling Using New Heuristic Methods

Air temperature prediction using different machine learning models

Prediction of Suspended Sediment Load Using Data-Driven Models

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