scholarly journals Suspended Sediment Load Simulation during Flood Events Using Intelligent Systems: A Case Study on Semiarid Regions of Mediterranean Basin

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3539
Author(s):  
Zaki Abda ◽  
Bilel Zerouali ◽  
Muwaffaq Alqurashi ◽  
Mohamed Chettih ◽  
Celso Augusto Guimarães Santos ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Suspended Sediment ◽  
Intelligent Systems ◽  
Sediment Load ◽  
Particle Swarm ◽  
Natural Phenomenon ◽  
Suspended Sediment Load ◽  
Flood Events ◽  
Swarm Optimization ◽  
Inference System

Sediment transport in rivers is a nonlinear natural phenomenon, which can harm the environment and hydraulic structures and is one of the main reasons for the dams’ siltation. In this paper, the following artificial intelligence approaches were used to simulate the suspended sediment load (SSL) during periods of flood events in the northeastern Algerian river basins: artificial neural network combined with particle swarm optimization (ANN-PSO), adaptive neuro-fuzzy inference system combined with particle swarm optimization (ANFIS-PSO), random forest (RF), and long short-term memory (LSTM). The comparison of the prediction accuracies of such different intelligent system approaches revealed that ANN-PSO, RF, and LSTM satisfactorily simulated the nonlinear process of SSL. Carefully comparing the results, the ANN-PSO model showed a slight superiority over the RF and LSTM models, with RMSE = 67.2990 kg/s in the Chemourah basin and RMSE = 55.8737 kg/s in the Gareat el tarf basin.

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).

Adaptive Shewhart Control Charts Under Fuzzy Parameters with Tuned Particle Swarm Optimization Algorithm

2021 ◽  
pp. 1-36
Author(s):  
Amirhossein Amiri ◽  
Ali Salmasnia ◽  
Meraj Zarifi ◽  
Mohammad Reza Maleki
Keyword(s):  
Particle Swarm Optimization ◽  
Control Chart ◽  
Adaptive Sampling ◽  
Control Charts ◽  
Particle Swarm ◽  
Design Parameters ◽  
Swarm Optimization ◽  
Inference System ◽  
Scale Parameters ◽  
Adaptive Control Charts

In recent years, adaptive control charts in which the design parameters depend on the observed samples have been successfully used as efficient alternatives for traditional control charts with constant parameters. In crisp run control rules, the process state may change very sharply from in-control to out-of-control conditions which increase the rate of false alarms. To overcome this drawback, this paper presents an adaptive Shewhart-type control chart, where the design parameters (sample size ([Formula: see text]), sampling interval ([Formula: see text]), and control limit coefficients ([Formula: see text] and [Formula: see text])) are defined with linguistic variables. To accomplish that, the chart parameters are determined based on the location of eight previous chart statistics using a set of fuzzy rules in a continuous environment. In order to improve the sensitivity of the proposed control chart in detecting small shifts in both location and scale parameters, the adaptive procedure is designed by integration of fuzzy Western Electric rules and fuzzy adaptive sampling rules. After designing the control charts using a fuzzy inference system (FIS), in order to provide an economic design of the proposed control chart, a tuned Particle Swarm Optimization (PSO) algorithm is employed to determine the optimal values corresponding to membership functions of the control chart parameters. Finally, using simulation studies, the capability of the proposed control chart is analyzed and compared with common charts in the literature. The results confirm that under different shifts in location and scale parameters, the proposed control chart outperforms other charts in terms of both economic and statistical criteria.

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 A OPTIMIZATION TUNED ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR DAM DEFORMATION PREDICTION

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1207-1213
Author(s):  
X. Y. Zhang ◽  
B. Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Deformation Prediction ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Dam Deformation

Abstract. The performance and stability of Adaptive Neuro-Fuzzy Inference System (ANFIS) depend on its network structure and preset parameter selection, and Particle Swarm Optimization-ANFIS (PSO-ANFIS) easily falls into the local optimum and is imprecise. A novel ANFIS algorithm tuned by Chaotic Particle Swarm Optimization (CPSO-ANFIS) is proposed to solve these problems. A chaotic ergodic algorithm is first used to improve the PSO and obtain a CPSO algorithm, and then the CPSO is used to optimize the parameters of ANFIS to avoid falling into the local optimum and improve the performance of ANFIS. Based on the deformation data from the Xiaolangdi Dam in China, three neural network algorithms, ANFIS, PSO-ANFIS, and CPSO-ANFIS, are used to establish the dam deformation prediction models after data preparation and selection of influencing factors for the dam deformation. The results are compared using evaluation indicators that show that CPSO-ANFIS is more accurate and stable than ANFIS and PSO-ANFIS both in predictive ability and in predicted results.

Sign in / Sign up

Export Citation Format

Share Document