Prediction of Suspended Sediment Load Using Data-Driven Models

Adnan, Rana Muhammad; Liang, Zhongmin; El-Shafie, Ahmed; Zounemat‐Kermani, Mohammad; Kişi, Özgür

doi:10.3390/w11102060

Cited by 56 publications

(24 citation statements)

References 54 publications

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“…Changes in the climate system balance increase the importance of evaluation of climate change effects on hydrological parameters. On the other hand, climate prediction is necessary for water resources sustainable management [1][2][3][4]. By creating General Circulation Models (GCM), climate conditions can be assessed for long-time scales.…”

Section: Introductionmentioning

confidence: 99%

Investigation into the Effects of Climate Change on Reference Evapotranspiration Using the HadCM3 and LARS-WG

Bayatvarkeshi

Zhang

Fasihi

et al. 2020

Water

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This study evaluates the effect of climate change on reference evapotranspiration (ET0), which is one of the most important variables in water resources management and irrigation scheduling. For this purpose, daily weather data of 30 Iranian weather stations from 1981 and 2010 were used. The HadCM3 statistical model was applied to report the output subscale of LARS-WG and to predict the weather information by A1B, A2, and B1 scenarios in three periods: 2011–2045, 2046–2079, and 2080–2113. The ET0 values were estimated by the Ref-ET software. The results indicated that the ET0 will rise from 2011 to 2113 approximately in all stations under three scenarios. The ET0 changes percentages in the A1B scenario during three periods from 2011 to 2113 were found to be 0.98%, 5.18%, and 12.17% compared to base period, respectively, while for the B1 scenario, they were calculated as 0.67%, 4.07%, and 6.61% and for the A2 scenario, they were observed as 0.59%, 5.35%, and 9.38%, respectively. Thus, the highest increase of the ET0 will happen from 2080 to 2113 under the A1B scenario; however, the lowest will occur between 2046 and 2079 under the B1 scenario. Furthermore, the assessment of uncertainty in the ET0 calculated by the different scenarios showed that the ET0 predicted under the A2 scenario was more reliable than the others. The spatial distribution of the ET0 showed that the highest ET0 amount in all scenarios belonged to the southeast and the west of the studied area. The most noticeable point of the results was that the ET0 differs from one scenario to another and from a period to another.

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Section: Introductionmentioning

confidence: 99%

Investigation into the Effects of Climate Change on Reference Evapotranspiration Using the HadCM3 and LARS-WG

Bayatvarkeshi

Zhang

Fasihi

et al. 2020

Water

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“…The MARS model is one of the sophisticated AI models, as it has the ability to provide a non-parametric feature that is able to identify the actual relationship between predictors and predicted using splines method for detecting the nonlinearity pattern [46]. The MARS model has been successfully applied in many hydrological applications [47][48][49][50][51][52]. The MARS model was successfully used to predict water pollution by Kisi and Parmar [47], to forecast sediment load by Adnan et al [48], to model daily streamflow by Yin et al [49], to predict evaporation by Ghaemi et al, [50], and to predict monthly river flow by Adnan et al [51].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Adnan

Heddam

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2020

Sustainability

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The potential or reference evapotranspiration (ET0) is considered as one of the fundamental variables for irrigation management, agricultural planning, and modeling different hydrological pr°Cesses, and therefore, its accurate prediction is highly essential. The study validates the feasibility of new temperature based heuristic models (i.e., group method of data handling neural network (GMDHNN), multivariate adaptive regression spline (MARS), and M5 model tree (M5Tree)) for estimating monthly ET0. The outcomes of the newly developed models are compared with empirical formulations including Hargreaves-Samani (HS), calibrated HS, and Stephens-Stewart (SS) models based on mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency. Monthly maximum and minimum temperatures (Tmax and Tmin) observed at two stations in Turkey are utilized as inputs for model development. In the applications, three data division scenarios are utilized and the effect of periodicity component (PC) on models’ accuracies are also examined. By importing PC into the model inputs, the RMSE accuracy of GMDHNN, MARS, and M5Tree models increased by 1.4%, 8%, and 6% in one station, respectively. The GMDHNN model with periodic input provides a superior performance to the other alternatives in both stations. The recommended model reduced the average error of MARS, M5Tree, HS, CHS, and SS models with respect to RMSE by 3.7–6.4%, 10.7–3.9%, 76–75%, 10–35%, and 0.8–17% in estimating monthly ET0, respectively. The HS model provides the worst accuracy while the calibrated version significantly improves its accuracy. The GMDHNN, MARS, M5Tree, SS, and CHS models are also compared in estimating monthly mean ET0. The GMDHNN generally gave the best accuracy while the CHS provides considerably over/under-estimations. The study indicated that the only one data splitting scenario may mislead the modeler and for better validation of the heuristic methods, more data splitting scenarios should be applied.

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“…Conceptually, the DENFIS is designed to generate and update new fuzzy rules during the learning of the system. Such a procedure allows the DENFIS to calculate the desired output according to them-most activated rules, which have been chosen dynamically from the set of the fuzzy rules [ 27 , 28 , 29 ]. It could be noticed from the DENFIS structure and procedure and the nature of ETo that the DENFIS model has the potential to adequately and effectively estimate the ETo.…”

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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Prediction of Suspended Sediment Load Using Data-Driven Models

Cited by 56 publications

References 54 publications

Investigation into the Effects of Climate Change on Reference Evapotranspiration Using the HadCM3 and LARS-WG

Investigation into the Effects of Climate Change on Reference Evapotranspiration Using the HadCM3 and LARS-WG

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Reference Evapotranspiration Modeling Using New Heuristic Methods

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