scholarly journals A new approach for modeling suspended sediment: Evolutionary fuzzy approach

2016 ◽  
Author(s):  
Ozgur Kisi
Keyword(s):  
Suspended Sediment ◽  
Fuzzy Inference ◽  
Suspended Sediments ◽  
Sediment Concentration ◽  
Inference System ◽  
Daily Streamflow ◽  
Fuzzy C Means Clustering ◽  
Artificial Neural Network Ann ◽  
Sediment Data ◽  
Mean Square Errors

Abstract. This paper proposes the application of evolutionary fuzzy (EF) approach for prediction of daily suspended sediment concentration (SSC). The EF was improved by the combination of two methods, fuzzy logic and genetic algorithm. The accuracy of EF models is compared with those of the artificial neural network (ANN) and adaptive neuro-fuzzy inference system with fuzzy c-means clustering (ANFIS-FCM). The daily streamflow and suspended sediment data collected from two stations on the Eel River in California, United States are used in the study. Root mean square errors (RMSE), mean absolute errors (MAE) and determination coefficient criteria are used for evaluating the accuracy of the models. The EF is found to be superior to the ANN and ANFIS-FCM in SSC prediction. The relative RMSE and MAE differences between the optimal EF and ANN models were found to be 13–50 % and 15–65 % for the upstream and downstream stations, respectively. Comparison of the optimal EF, ANN and ANFIS-FCM models in estimating peak and total suspended sediments revealed that the EF model provided better accuracy than the ANN and ANFIS-FCM.

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 Integration of ANFIS with PCA and DWT for daily suspended sediment concentration prediction

Water SA ◽  
2021 ◽  
Vol 47 (2 April) ◽  
Author(s):  
Nguyen Mai Dang ◽  
Duong Tran Anh
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Fuzzy Inference ◽  
Ecological Integrity ◽  
Principal Component ◽  
Sediment Concentration ◽  
The United States ◽  
Discrete Wavelet ◽  
Coupled Models ◽  
Inference System

Quantifying sediment load is vital for aquatic and riverine biota and has been the subject of various environmental studies since sediment plays a key role in maintaining ecological integrity, river morphology and agricultural productivity. However, predicting sediment concentration in rivers is difficult because of the non-linear relationships of flow rates, geophysical characteristics and sediment loads. It is thus very important to propose suitable statistical methods which can provide fast, accurate and robust prediction of suspended sediment concentration (SSC) for management guidance. In this study, we developed coupled models of discrete wavelet transform (DWT) with adaptive neuro-fuzzy inference system (ANFIS), named DWT-ANFIS, and principal component analysis (PCA) with ANFIS, named PCA-ANFIS, for SSC time-series modeling. The coupled models and single ANFIS model were trained and tested using long-term daily SSC and river discharge which were measured on the Schuylkill and Iowa Rivers in the United States. The findings showed that the PCA-ANFIS performed better than the single ANFIS and the coupled DWT-ANFIS. Further applications of the PCA-ANFIS should be considered for simulation and prediction of other indicators relating to weather, water resources, and the environment.

scholarly journals Classifications of runoff and sediment data to improve the rating curve method

2017 ◽  
Vol 48 ◽  
Author(s):  
Hossein Khaledian ◽  
Homayoun Faghih ◽  
Ata Amini
Keyword(s):  
Suspended Sediment ◽  
River Flow ◽  
Data Classification ◽  
Suspended Sediments ◽  
Sediment Concentration ◽  
Low Flow ◽  
Rating Curve ◽  
Sediment Rating Curve ◽  
Curve Method ◽  
Sediment Data

In this study, data classification method was evaluated to increase accuracy of estimating suspended sediment load. To achieve this objective, suspended sediment in Chehelgazi and Khalifeh-Tarkhan rivers in Kurdistan, Iran, were estimated using Sediment Rating Curve (SRC) method in three different approaches of data classification. At first, measured data were modeled without classification. Then, data based on flow statues were divided into two series as high and low flow. Eventually, based on sediment concentration, the data were divided into low and high sediment concentration. Long-term runoff and sediment data were used to calibrate rating curve model. The estimated values were compared with recorded data and the performances of these models were evaluated using statistical criteria. The results indicated an effective role of data classification to improve estimating sediment transportation by rating curve method. In one of the stations, it was observed that due to classification based on river flow and sediment concentration, model efficiency was increased about 45% and 28%, respectively. Furthermore, in case of improving efficiency of SRC method, classifying data based on flow statues was found to be more effective than sediment concentration. The results of this study can be used to improve the management of the basin by more accurately estimating the amount of suspended sediments transporting in the rivers draining to reservoirs.

scholarly journals Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 286 ◽  
Author(s):  
Athanasios Bogiatzis ◽  
Basil Papadopoulos
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Initial Step ◽  
Local Area ◽  
Mathematical Functions ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Entropy Measures ◽  
Complex Procedure ◽  
Artificial Neural Network Ann

Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures which we efficiently managed to use on both global and local thresholding. The general method which we presented was an open and adaptable procedure, it was free of sensitivity or bias parameters and it involved image classification, mathematical functions, a fuzzy symmetrical triangular number and some criteria of choosing between two possible thresholds. Here, we continue this research and try to avoid all these by automatically connecting our measures with the wanted threshold using some Artificial Neural Network (ANN). Using an ANN in image segmentation is not uncommon especially in the domain of medical images. However, our proposition involves the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) which means that all we need is a proper database. It is a simple and immediate method which could provide researchers with an alternative approach to the thresholding problem considering that they probably have at their disposal some appropriate and specialized data.

Comparision of some models for estimation of reflectance of hypericum leaves under stress conditions

2014 ◽  
Vol 9 (12) ◽  
pp. 1226-1234
Author(s):  
Kadir Temizel ◽  
Mehmet Odabas ◽  
Nurettin Senyer ◽  
Gokhan Kayhan ◽  
Sreekala Bajwa ◽  
...  
Keyword(s):  
High Performance ◽  
Fuzzy Inference ◽  
High Water ◽  
Stress Conditions ◽  
Ann Model ◽  
Inference System ◽  
Leaf Reflectance ◽  
Neuro Fuzzy ◽  
Salinity Levels ◽  
Artificial Neural Network Ann

AbstractLack of water resources and high water salinity levels are among the most important growth-restricting factors for plants species of the world. This research investigates the effect of irrigation levels and salinity on reflectance of Saint John’s wort leaves (Hypericum perforatum L.) under stress conditions (water and salt stress) by multiple linear regression (MLR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Empirical and heuristics modeling methods were employed in this study to relate stress conditions to leaf reflectance. It was found that the constructed ANN model exhibited a high performance than multiple regression and ANFIS in estimating leaf reflectance accurately.

scholarly journals Curvas-chave de descargas de sedimentos em suspensão no Baixo São Francisco

2020 ◽  
Vol 13 (3) ◽  
pp. 1248 ◽  
Author(s):  
Solange Cavalcanti de Melo ◽  
José Coelho de Araújo Filho ◽  
Renata Maria Caminha Mendes de Oliveira Carvalho
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Suspended Sediments ◽  
Sediment Concentration ◽  
Gradual Reduction ◽  
Sao Francisco River ◽  
Sediment Loads ◽  
São Francisco River ◽  
Historical Series ◽  
Suspended Sediment Concentrations

RESUMOO conhecimento da análise quantitativa das concentrações de sedimentos em suspensão transportados pelo rio São Francisco bem como sua relação com as vazões é de muita importância, pois pode auxiliar na identificação dos efeitos da intervenção humana e ou ocasionados pelas condições naturais da região. As regiões a jusante dos barramentos no rio São Francisco apresentam como principal consequência a regularização das vazões e a diminuição das concentrações de sedimentos. O objetivo da pesquisa foi determinar as curvas-chave de sedimentos em suspensão (CCS) nas estações fluviométricas instaladas no Baixo São Francisco (BSF) após a barragem de Xingó. Para o estabelecimento dessas curvas foram utilizados dados de vazão e concentração de sedimentos em suspensão, obtidos do sistema Hidroweb no site da Agência Nacional da Água (ANA) no período de 1999 a 2018. Foram obtidas CCS para todo o trecho do BSF as quais apresentaram bons coeficientes de determinação. Na análise dos dados também foi possível perceber que nos últimos anos, desde 2013 houve redução gradativa das vazões disponibilizadas na barragem de Xingó. Consequentemente, houve também a redução gradativa das cargas de sedimentos em suspensão geradas nas estações de Piranhas, Traipu e Propriá, ou seja, os menores valores já registrados no BSF correspondendo as menores séries históricas tanto de vazão como de sedimentos em suspensão.  Keys curves of sediment discharges in suspension in the Lower São Francisco A B S T R A C TThe knowledge of the quantitative analysis of suspended sediment concentrations carried by the São Francisco River as well as its relation with the flows is of great importance, since it can help in the identification of the effects of human intervention and/or caused by the natural conditions of the region. In the downstream regions of the São Francisco riverbanks, the main consequence was the regularization of flow rates and the reduction of sediment concentrations. The objective of the research was to determine the key curves of suspended sediments (CCS) at the fluviometric stations installed in the lower São Francisco river after Xingó dam. For the evaluation, flow data and suspended sediment concentration were used. These data were obtained from the Hidroweb system on the website of the National Water Agency (ANA) from 1999 to 2018. CCS were plotted for all stretches and presented good coefficients of determination (R2). Based on the analysis of the data it was also possible to notice that in recent years, since 2013 there has been a gradual reduction of the flows available in the Xingó dam. Consequently, there was also a gradual reduction of suspended sediment loads generated at the Piranhas, Traipu and Propriá stations, that is, the lowest values already recorded in lower São Francisco, corresponding to the lower historical series of both discharge and suspended sediments.Keywords: dam, flow, sediments 

scholarly journals Failure prediction of e-banking application system using adaptive neuro fuzzy inference system (ANFIS)

2019 ◽  
Vol 9 (1) ◽  
pp. 667 ◽  
Author(s):  
Yuwono Abdillah ◽  
Suharjito Suharjito
Keyword(s):  
Network Performance ◽  
Fuzzy Inference ◽  
Response Times ◽  
Banking System ◽  
Internet Banking ◽  
Inference System ◽  
Banking Service ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering ◽  
Operation Unit

Problems often faced by IT operation unit is the difficulty in determining the cause of the failure of an incident such as slowing access to the internet banking url, non-functioning of some features of m-banking or even the cessation of the entire e-banking service. The proposed method to modify ANFIS with Fuzzy C-Means Clustering (FCM) approach is applied to detect four typical kinds of faults that may happen in the e-banking system, which are application response times, transaction per second, server utilization and network performance. Input data is obtained from the e-banking monitoring results throughout 2017 that become data training and data testing. The study shows that an ANFIS modeling with FCM optimized input has a RMSE 0.006 and  increased accuracy by 1.27% compared to ANFIS without FCM optimization.

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