Artificial intelligence simulation of suspended sediment load with different membership functions of ANFIS

2020 ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Azam Marjani ◽  
Saeed Shirazian
Keyword(s):  
Artificial Intelligence ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Suspended Sediment Load ◽  
Membership Functions

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 Estimation of Suspended Sediment Load Using Artificial Intelligence-Based Ensemble Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Vahid Nourani ◽  
Huseyin Gokcekus ◽  
Gebre Gelete
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Ensemble Methods ◽  
Performance Measure ◽  
Combination Method ◽  
Suspended Sediment Load ◽  
Anfis Model ◽  
Ensemble Models

Suspended sediment modeling is an important subject for decision-makers at the catchment level. Accurate and reliable modeling of suspended sediment load (SSL) is important for planning, managing, and designing of water resource structures and river systems. The objective of this study was to develop artificial intelligence- (AI-) based ensemble methods for modeling SSL in Katar catchment, Ethiopia. In this paper, three single AI-based models, that is, support vector machine (SVM), adaptive neurofuzzy inference system (ANFIS), feed-forward neural network (FFNN), and one conventional multilinear regression (MLR) modes, were used for SSL modeling. Besides, four different ensemble methods, neural network ensemble (NNE), ANFIS ensemble (AE), weighted average ensemble (WAE), and simple average ensemble (SAE), were developed by combining the outputs of the four single models to improve their predictive performance. The study used two-year (2016-2017) discharge and SSL data for training and verification of the applied models. Determination coefficient (DC) and root mean square error (RMSE) were used to evaluate the performances of the developed models. Based on the performance measure results, the ANFIS model provides higher efficiency than the other developed single models. Out of all developed ensemble models, the nonlinear ANFIS model combination method was found to be the most accurate method and could increase the efficiency of SVM, MLR, ANFIS, and FFNN models by 19.02%, 37%, 9.73%, and 16.3%, respectively, at the verification stage. Overall, the proposed ensemble models in general and the AI-based ensemble in particular provide excellent performance in SSL estimation.

Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method

2020 ◽  
Vol 65 (12) ◽  
pp. 2116-2127
Author(s):  
Viet-Ha Nhu ◽  
Khabat Khosravi ◽  
James R. Cooper ◽  
Mahshid Karimi ◽  
Ozgur Kisi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Suspended Sediment ◽  
Intelligence Testing ◽  
Sediment Load ◽  
Suspended Sediment Load ◽  
Random Subspace ◽  
Subspace Method ◽  
Load Prediction ◽  
Random Subspace Method

Artificial intelligence for suspended sediment load prediction: a review

2021 ◽  
Vol 80 (9) ◽  
Author(s):  
Deepak Gupta ◽  
Barenya Bikash Hazarika ◽  
Mohanadhas Berlin ◽  
Usha Mary Sharma ◽  
Kshitij Mishra
Keyword(s):  
Artificial Intelligence ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Suspended Sediment Load ◽  
Load Prediction

scholarly journals Land-Use/-Cover Changes and Their Effect on Soil Erosion and River Suspended Sediment Load in Different Landscape Zones of European Russia during 1970–2017

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1631
Author(s):  
Artyom V. Gusarov
Keyword(s):  
Soviet Union ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Gully Erosion ◽  
European Russia ◽  
Relative Reduction ◽  
Forest Steppe ◽  
Suspended Sediment Load ◽  
Cultivated Land ◽  
Study Region

Contemporary trends in cultivated land and their influence on soil/gully erosion and river suspended sediment load were analyzed by various landscape zones within the most populated and agriculturally developed part of European Russia, covering 2,222,390 km2. Based on official statistics from the Russian Federation and the former Soviet Union, this study showed that after the collapse of the Soviet Union in 1991, there was a steady downward trend in cultivated land throughout the study region. From 1970–1987 to 2005–2017, the region lost about 39% of its croplands. Moreover, the most significant relative reduction in cultivated land was noted in the forest zone (south taiga, mixed and broadleaf forests) and the dry steppes and the semi-desert of the Caspian Lowland—about 53% and 65%, respectively. These territories are with climatically risky agriculture and less fertile soils. There was also a widespread reduction in agricultural machinery on croplands and livestock on pastures of the region. A decrease in soil/gully erosion rates over the past decades was also revealed based on state hydrological monitoring data on river suspended sediment load as one of the indicators of the temporal variability of erosion intensity in river basins and the published results of some field research in various parts of the studied landscape zones. The most significant reduction in the intensity of erosion and the load of river suspended sediment was found in European Russia’s forest-steppe zone. This was presumably due to a favorable combination of the above changes in land cover/use and climate change.

scholarly journals Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models

2021 ◽  
Author(s):  
Hamid Darabi ◽  
Sedigheh Mohamadi ◽  
Zahra Karimidastenaei ◽  
Ozgur Kisi ◽  
Mohammad Ehteram ◽  
...  
Keyword(s):  
Suspended Sediment ◽  
Soft Computing ◽  
Sediment Load ◽  
Optimization Algorithms ◽  
Training Phase ◽  
Suspended Sediment Load ◽  
Engineering Structures ◽  
Northern Iran ◽  
Taylor Diagram ◽  
Computing Models

AbstractAccurate modeling and prediction of suspended sediment load (SSL) in rivers have an important role in environmental science and design of engineering structures and are vital for watershed management. Since different parameters such as rainfall, temperature, and discharge with the different lag times have significant effects on the SSL, quantifying and understanding nonlinear interactions of the sediment dynamics has always been a challenge. In this study, three soft computing models (multilayer perceptron (MLP), adaptive neuro-fuzzy system (ANFIS), and radial basis function neural network (RBFNN)) were used to predict daily SSL. Four optimization algorithms (sine–cosine algorithm (SCA), particle swarm optimization (PSO), firefly algorithm (FFA), and bat algorithm (BA)) were used to improve the capability of SSL prediction of the models. Data from gauging stations at the mouth of the Kasilian and Talar rivers in northern Iran were used in the analysis. The selection of input combinations for the models was based on principal component analysis (PCA). Uncertainty in sequential uncertainty fitting (SUFI-2) and performance indicators were used to assess the potential of models. Taylor diagrams were used to visualize the match between model output and observed values. Assessment of daily SSL predictions for Talar station revealed that ANFIS-SCA yielded the best results (RMSE (root mean square error): 934.2 ton/day, MAE (mean absolute error): 912.2 ton/day, NSE (Nash–Sutcliffe efficiency): 0.93, PBIAS: 0.12). ANFIS-SCA also yielded the best results for Kasilian station (RMSE: 1412.10 ton/day, MAE: 1403.4 ton/day, NSE: 0.92, PBIAS: 0.14). The Taylor diagram confirmed that ANFIS-SCA achieved the best match between observed and predicted values for various hydraulic and hydrological parameters at both Talar and Kasilian stations. Further, the models were tested in Eagel Creek Basin, Indiana state, USA. The results indicated that the ANFIS-SCA model reduced RMSE by 15% and 21% compared to the MLP-SCA and RBFNN-SCA models in the training phase. Comparing models performance indicated that the ANFIS-SCA model could decrease MAE error compared to ANFIS-BA, ANFIS-PSO, ANFIS-FFA, and ANFIS models by 18%, 32%, 37%, and 49% in the training phase, respectively. The results indicated that the integration of optimization algorithms and soft computing models can improve the ability of models for predicting SSL. Additionally, the hybridization of soft computing models with optimization algorithms can decrease the uncertainty of models.

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