Prediction of Local Scour Depth Downstream of Bed Sills Using Soft Computing Models

2014 ◽  
pp. 197-208 ◽  
Author(s):  
A. Zahiri ◽  
H. Md. Azamathulla ◽  
Kh. Ghorbani
Keyword(s):  
Soft Computing ◽  
Local Scour ◽  
Scour Depth ◽  
Computing Models

Experimental Study and Prediction Maximum Scour Depth Equation of Local Scour Around Bridge Pier

2018 ◽  
Author(s):  
Mohammed Tareq Shukri ◽  
M. Günal ◽  
Junaid Kameran Ahmed
Keyword(s):  
Experimental Study ◽  
Local Scour ◽  
Bridge Pier ◽  
Scour Depth

scholarly journals Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Masoud Haghbin ◽  
Ahmad Sharafati ◽  
Davide Motta ◽  
Nadhir Al-Ansari ◽  
Mohamadreza Hosseinian Moghadam Noghani
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Soft Computing ◽  
Sea Surface ◽  
Linear Processes ◽  
Comprehensive Review ◽  
Droughts And Floods ◽  
Input Variables ◽  
And Performance ◽  
Computing Models

AbstractThe application of soft computing (SC) models for predicting environmental variables is widely gaining popularity, because of their capability to describe complex non-linear processes. The sea surface temperature (SST) is a key quantity in the analysis of sea and ocean systems, due to its relation with water quality, organisms, and hydrological events such as droughts and floods. This paper provides a comprehensive review of the SC model applications for estimating SST over the last two decades. Types of model (based on artificial neural networks, fuzzy logic, or other SC techniques), input variables, data sources, and performance indices are discussed. Existing trends of research in this field are identified, and possible directions for future investigation are suggested.

PREDICTION OF SCOUR DEPTH IN DOWNSTREAM OF SKI-JUMP SPILLWAYS USING SOFT COMPUTING TECHNIQUES

2011 ◽  
Vol 33 (1) ◽  
Author(s):  
Mohammad K. Ayoubloo ◽  
Hazi Md. Azamathulla ◽  
Zulfequar Ahmad ◽  
Aminuddin Ab. Ghani ◽  
Javad Mahjoobi ◽  
...  
Keyword(s):  
Soft Computing ◽  
Scour Depth ◽  
Soft Computing Techniques

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.

scholarly journals Emerging Trends in Soft Computing Models in Bioinformatics and Biomedicine

2014 ◽  
Vol 2014 ◽  
pp. 1-3 ◽  
Author(s):  
Yudong Zhang ◽  
Saeed Balochian ◽  
Vishal Bhatnagar
Keyword(s):  
Soft Computing ◽  
Emerging Trends ◽  
Computing Models
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