Real-time prediction of water level change using adaptive neuro-fuzzy inference system

Kaloop, Mosbeh R.; El-Diasty, Mohammed; Hu, Jong Wan

doi:10.1080/19475705.2017.1327464

Cited by 15 publications

(6 citation statements)

References 26 publications

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“…In other words, ANFIS is defined as a system that uses fuzzy rules and the associated fuzzy inference method for learning and rule optimization purposes [46]. In this study, we apply the combined learning rule, which uses conventional back-propagation and least square methods to estimate the parameters of the model accurately [47]. e model is made up of five layers.…”

Section: Adaptive Neurofuzzy Interference System (Anfis)mentioning

confidence: 99%

Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

Mustafa

Liu

et al. 2020

Advances in Civil Engineering

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The soft computing models used for predicting land surface temperature (LST) changes are very useful to evaluate and forecast the rapidly changing climate of the world. In this study, four soft computing techniques, namely, multivariate adaptive regression splines (MARS), wavelet neural network (WNN), adaptive neurofuzzy inference system (ANFIS), and dynamic evolving neurofuzzy inference system (DENFIS), are applied and compared to find the best model that can be used to predict the LST changes of Beijing area. The topographic change is considered in this study to accurately predict LST; furthermore, Landsat 4/5 TM and Landsat 8OLI_TIRS images for four years (1995, 2004, 2010, and 2015) are used to study the LST changes of the research area. The four models are assessed using statistical analysis, coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) in the training and testing stages, and MARS is used to estimate the important variables that should be considered in the design models. The results show that the LST for the studied area increases by 0.28°C/year due to the urban changes in the study area. In addition, the topographic changes and previously recorded temperature changes have a significant influence on the LST prediction of the study area. Moreover, the results of the models show that the MARS, ANFIS, and DENFIS models can be used to predict the LST of the study area. The ANFIS model showed the highest performances in the training (R2 = 0.99, RMSE = 0.78°C, MAE = 0.55°C) and testing (R2 = 0.99, RMSE = 0.36°C, MAE = 0.16°C) stages; therefore, the ANFIS model can be used to predict the LST changes in the Beijing area. The predicted LST shows that the change in climate and urban area will affect the LST changes of the Beijing area in the future.

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Section: Adaptive Neurofuzzy Interference System (Anfis)mentioning

confidence: 99%

Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

Mustafa

Liu

et al. 2020

Advances in Civil Engineering

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“…Additionally, [27] employed the ANFIS model to predict Water Level Change (WLC) models of a month of hourly WLC for Yarmouth, Sain-John, and Charlottetown stations in Canadian waters, and then compared it with the results obtained using the wavelet neural network (WNN) model. The results obtained showed that the ANFIS model is superior.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating the Influence of Meteorological Parameters on the Accuracy of Sea-Level Prediction Models in Sabah, Malaysia

et al. 2020

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This study aims to investigate the impact of meteorological parameters such as wind direction, wind speed, rainfall, and mean cloud cover on sea-level rise projections for different time horizons—2019, 2023, 2028, 2048, and 2068—at three stations located in Kudat, Sandakan, and Kota Kinabalu, which are districts in the state of Sabah, Malaysia. Herein, two different scenarios, scenario1 (SC1) and scenario2 (SC2), were investigated, with each scenario comprising a different combination of input parameters. This study proposes two artificial intelligence techniques: a multilayer perceptron neural network (MLP-ANN) and an adaptive neuro-fuzzy inference system (ANFIS). Furthermore, three evaluation indexes were adopted to assess the performance of the proposed models. These indexes are the correlation coefficient, root mean square error, and scatter index. The trial and error method were used to tune the hyperparameters: the number of neurons in the hidden layer, training algorithms, transfer and activation functions, and number and shape of the membership function for the proposed models. Results show that for the above mentioned three stations, the ANFIS model outperformed MLP-ANN by 0.740%, 6.23%, and 9.39%, respectively. To assess the uncertainties of the best model, ANFIS, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPUs) and the band width of 95 percent confidence intervals (d-factors) are selected. The obtained values bracketed by 95PPUs are show about 75.2%, 77.4%, 76.8% and the d-factor has a value of 0.27, 0.21 and 0.23 at Kudat, Sandakan and Kota Kinabalu stations, respectively. A comparison between the two scenarios shows that SC1 achieved a high level of accuracy on Kudat and Sandakan data, whereas SC2 outperformed SC1 on Kota Kinabalu data.

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“…Soft computing techniques have successfully used in the last three decades to solve different complex hydrological problems (Daliakopoulos et al, 2005;Ehteram et al, 2021;Hadi et al, 2019;Kaloop et al, 2017;Malik et al, 2020b;Parsaie et al, 2015;Sammen et al, 2017;Singh et al, 2018;Tikhamarine et al, 2020c;Yaseen et al, 2020b;Young et al, 2015). For water level prediction, several techniques have been used such as Artificial Neural Networks (ANN) (Alvisi et al, 2006), Autoregressive Integrated Moving Average (ARIMA) (Reza et al, 2018;Sihag et al, 2020;Xu et al, 2019), and Support Vector Machine (SVM) (Khan & Coulibaly, 2006;Liong & Sivapragasam, 2002).…”

Section: Introductionmentioning

confidence: 99%

Prediction of daily water level using new hybridized GS-GMDH and ANFIS-FCM models

Ebtehaj

Sammen

Sidek

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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Accurate prediction of water level (WL) is essential for the optimal management of different water resource projects. The development of a reliable model for WL prediction remains a challenging task in water resources management. In this study, novel hybrid models, namely, Generalized Structure-Group Method of Data Handling (GS-GMDH) and Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM) were proposed to predict the daily WL at Telom and Bertam stations located in Cameron Highlands of Malaysia. Different percentage ratio for data division i.e. 50%-50% (scenario-1), 60%-40% (scenario-2), and 70%-30% (scenario-3) were adopted for training and testing of these models. To show the efficiency of the proposed hybrid models, their results were compared with the standalone models that include the Gene Expression Programming (GEP) and Group Method of Data Handling (GMDH). The results of the investigation revealed that the hybrid GS-GMDH and ANFIS-FCM models outperformed the standalone GEP and GMDH models for the prediction of daily WL at both study sites. In addition, the results indicate the best performance for WL prediction was obtained in scenario-3 (70%-30%). In summary, the results highlight the better suitability and supremacy of the proposed hybrid GS-GMDH and ANFIS-FCM models in daily WL prediction, and can, serve as robust and reliable predictive tools for the study region.

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Real-time prediction of water level change using adaptive neuro-fuzzy inference system

Cited by 15 publications

References 26 publications

Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

Investigating the Influence of Meteorological Parameters on the Accuracy of Sea-Level Prediction Models in Sabah, Malaysia

Prediction of daily water level using new hybridized GS-GMDH and ANFIS-FCM models

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