scholarly journals Modeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy

2021 ◽  
Author(s):  
Saeed Farzin ◽  
Mahdi Valikhan Anaraki
Keyword(s):  
Climate Change ◽  
Suspended Sediment ◽  
General Circulation ◽  
Sediment Load ◽  
Fuzzy Inference ◽  
Circulation Model ◽  
Support Vector ◽  
Suspended Sediment Load ◽  
Inference System ◽  
New Strategy

Abstract In the present study, for the first time, a new strategy based on a combination of the hybrid least-squares support-vector machine (LS-SVM) and flower pollination optimization algorithm (FPA), average 24 general circulation model (GCM) output, and delta change factor method has been developed to achieve the impacts of climate change on runoff and suspended sediment load (SSL) in the Lighvan Basin in the period (2020–2099). Also, the results of modeling were compared to those of LS-SVM and adaptive neuro-fuzzy inference system (ANFIS) methods. The comparison of runoff and SSL modeling results showed that the LS-SVM-FPA algorithm had the best results and the ANFIS algorithm had the worst results. After the acceptable performance of the LS-SVM-FPA algorithm was proved, the algorithm was used to predict runoff and SSL under climate change conditions based on ensemble GCM outputs for periods (2020–2034, 2035–2049, 2070–2084, and 2085–2099) under three scenarios of RCP2.6, RCP4.5, and RCP8.5. The results showed a decrease in the runoff in all periods and scenarios, except for the two near periods under the RCP2.6 scenario for runoff. The predicted runoff and SSL time series also showed that the SSL values were lower than the average observation period, except for 2036–2039 (up to an 8% increase in 2038).

scholarly journals Prediction of Suspended Sediment Load Using Data-Driven Models

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2060 ◽  
Author(s):  
Adnan ◽  
Liang ◽  
El-Shafie ◽  
Zounemat-Kermani ◽  
Kisi
Keyword(s):  
Suspended Sediment ◽  
Fuzzy Inference System ◽  
Sediment Load ◽  
Fuzzy Inference ◽  
Resource Planning ◽  
Multivariate Adaptive Regression Splines ◽  
Suspended Sediment Load ◽  
Inference System ◽  
Daily Streamflow ◽  
Neural Fuzzy

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.

scholarly journals Evaluation of different types of artificial intelligence methods to model the suspended sediment load in Tigris River

2018 ◽  
Vol 162 ◽  
pp. 03003 ◽  
Author(s):  
Mustafa Al-Mukhtar
Keyword(s):  
Artificial Intelligence ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Fuzzy Inference ◽  
Coefficient Of Determination ◽  
Efficiency Coefficient ◽  
Suspended Sediment Load ◽  
Inference System ◽  
Tigris River ◽  
Artificial Neural Network Ann

Modeling of suspended sediment load in rivers has a major role in a proper management of water resources. Artificial intelligence has been identified as an efficient way to model the complex nonlinear hydrological relationship. In this study, Adaptive Neuro Fuzzy Inference System (ANFIS), in addition to two different kinds of Artificial Neural Network (ANN) i.e. feedforward and radial basis networks were used and compared to model the suspended sediment load (SSL) in Tigris River-Baghdad using the streamflow discharge as input. To this end, an intermittent data of SSL and streamflow were collected over the period 1962-1981 from Sarai station in Baghdad. 70 % of these data was used to calibrate (train) the networks and the remaining 30% for the validation (test). The coefficient of determination (R2), root mean square error (RMSE), and Nash and Sutcliffe model efficiency coefficient (NSE) were used to judge whether the observed and modelled data belong to the same distribution. Results revealed that the ANFIS model outperform the other methods. R2, RMSE, and NSE of ANFIS during the calibration phase were equal to 0.58, 75617, and 0.58, respectively and during the validation were 0.72, 27944, and 0.59, respectively. Therefore, ANFIS approach is recommended to estimate the river suspended sediment load.

scholarly journals A comparison of artificial intelligence models for the estimation of daily suspended sediment load: a case study on the Telar and Kasilian rivers in Iran

2018 ◽  
Vol 19 (1) ◽  
pp. 165-178 ◽  
Author(s):  
Samad Emamgholizadeh ◽  
Razieh Karimi Demneh
Keyword(s):  
Suspended Sediment ◽  
Sediment Load ◽  
Fuzzy Inference ◽  
Gene Expression Programming ◽  
Coefficient Of Determination ◽  
Suspended Sediment Load ◽  
Inference System ◽  
Sediment Rating Curve ◽  
Intelligence Models ◽  
Better Than

Abstract The estimation of the suspended sediment load in rivers is one of the main issues in hydraulic engineering. Different traditional methods such as the sediment rating curve (SRC) can be used to estimate the suspended sediment load of rivers. The main problem with this method is its low accuracy and uncertainty. In this study, the ability of three intelligence models namely: gene expression programming (GEP), artificial neural networks (ANN) and adaptive neuro fuzzy inference system (ANFIS) were compared with the SRC method. The daily flow discharge and sediment discharge at two hydrometric stations of the Kasilian and Telar rivers in the period of 1964–2014 were used to develop intelligence models. The performance of these methods indicated that all intelligence models give reliable results in the estimation of the suspended sediment load and their performance was better than the SRC method. Moreover, results showed that the GEP model with a high coefficient of determination (R2) and a low mean absolute error (MAE) was better than both the ANN and ANFIS models for the estimation of daily suspended sediment load of the two sub-basins of the Kasilian and Telar rivers.

Simulation of Temperature and Precipitation under the Climate Change Scenarios

2016 ◽  
pp. 465-491
Author(s):  
Umut Okkan ◽  
Gul Inan
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Circulation Model ◽  
Analysis Data ◽  
Relevance Vector Machine ◽  
Support Vector ◽  
Climate Change Scenarios ◽  
Climate Change Effects ◽  
Temperature And Precipitation ◽  
Artificial Neural Network Ann

This study aims to discuss the potentials of machine learning methods such as artificial neural network (ANN), least squares support vector machine (LSSVM), and relevance vector machine (RVM) in downscaling of simulations of a general circulation model (GCM) for monthly temperature and precipitation of the Demirkopru Dam located in the Aegean region of Turkey. The predictors are obtained from ERA-Interim re-analysis data. The best performed downscaling model is integrated into European Centre Hamburg Model (ECHAM5) with A2 future scenario. The results are then discussed to assess the probable climate change effects on temperature and precipitation.

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