An ANFIS-based approach for predicting the bed load for moderately sized rivers

Azamathulla, Hazi Mohammad; Chang, Chun Kiat; Ghani, Aminuddin Ab.; Ariffin, Junaidah; Zakaria, Nor Azazi; Hasan, Zorkeflee Abu

doi:10.1016/j.jher.2008.10.003

Cited by 82 publications

(27 citation statements)

References 13 publications

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“…Recently ANFIS has been applied in many agricultural and environmental settings, and represents a useful framework to deal with various issues, such as: 1) complexity of the soil compaction processes and qualitative knowledge associated with site-specific soil compaction evaluation (de Araújo & Saraiva, 2003); 2) soil science (Akbarzadeh et al, 2009;Lee et al, 2003;McBratney & Odeh, 1997); 3) estimating soil erosion (Akbarzadeh et al, 2009a); 4) predicting daily reference evapotranspiration (Cai et al, 2004;Cai &Mu, 2005;Lin et al, 2007), daily irrigation water demand (Atsalakis and Minoudaki, 2007), and wind forecasts (Potter et al, 2004); 5) hydro-environmental research (Azamathulla et al, 2009;Peschel et al, 2002); 6) estimating fluvial nutrient loads in watersheds (Marce, 2004); 7) behavioral interest identification in farm mechanization development (Tooy & Murase, 2007); 8) modeling of crop yield prediction (Arkhipov et al, 2008(Arkhipov et al, , 2012Kurtener et al, 2005Kurtener et al, , 2006Stathakis et al, 2006); and 9) agricultural robots (Stathakis et al, 2006;Xie et al, 2007).…”

Section: Utilization Of Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Utilization of Fuzzy Computing in Agrophysics

Kurtener¹,

Yakushev²

2014

EAJ

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Traditionally decision of agrophysics problems was evaluated with problem-oriented models. Utilization of fuzzy computing gives new dimension for agrophsics. It is well known that in agricultural sciences there exists much fuzzy knowledge, that is, knowledge which is vague, imprecise, uncertain, ambiguous, inexact, or probabilistic in nature. Fuzzy Computing is power tools for dealing with randomness and uncertainties. This paper reviews exploitation of fuzzy computing in agrophysics. The emphasis is on the achievements of Agrophysical Research Institute, St. Petersburg, Russia, especially in the fields of soil tillage, crop management, precision agriculture, melioration, and agricultural engineering.

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Section: Utilization Of Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Utilization of Fuzzy Computing in Agrophysics

Kurtener¹,

Yakushev²

2014

EAJ

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“…In the backward pass, the consequent parameters are fixed and the gradient descent method is applied to update the premise parameters. Premise and consequent parameters will be identified for membership functions (MF) and the fuzzy inference system (FIS) by repeating the forward and backward passes (Melin and Castillo, 2005;Wei, 2007;Azamathulla et al, 2009).…”

Section: Anfismentioning

confidence: 99%

Estimation of Some Coal Parameters Depending on Petrographic and Inorganic Analyses by Using Genetic Algorithm and Adaptive Neuro-Fuzzy Inference Systems

Chelgani

Dehghan

Hower

2011

Energy Exploration & Exploitation

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Adaptive neuro-fuzzy inference systems (ANFIS) in combination with genetic algorithm (GA); provide valuable modeling approaches of complex systems for a wide range of coal samples. Evaluation of this combination (GA-ANFIS) showed that the GA-ANFIS approach can be utilized as an efficient tool for describing and estimating some of coal variables such as Hardgrove grindability index, gross calorific value, free swelling index, and maximum vitrinite reflectance with various coal analyses (proximate, ultimate, elemental, and petrographic analysis). Statistical factors (correlation coefficient, mean square error, and variance accounted for) and differences between actual and predicted values demonstrated that the GA-ANFIS can be applied successfully, and provide high accuracy for prediction of those coal variables.

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“…The basin became a first watershed in the country is initiated towards implementing of Integrated River Basin Management (IRBM) [19]. Many researchers were studied on the hydrological processes of the basin include a historical water discharges study [20]; the impact of land used change on discharge and direct runoff [21]- [24]; sustainable groundwater resources and environmental management [25]; the flood hazard mapping [26], [27]; the water supply [28], [29]; water quality [30] and a river bed properties study of the river basin [31], [32]. The most current in early 2014, the upper part of the basin was experienced the ammonia pollution due to the effluent Fig.…”

Section: Introductionmentioning

confidence: 99%

Application on One-at-a-Time Sensitivity Analysis of Semi-Distributed Hydrological Model in Tropical Watershed

Khalid¹,

Ali²,

Rahman³

et al. 2016

IJET

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Abstract-A soil and water assessment tool (SWAT) model has been employed for the Langat River basin, Malaysia to predict stream flows. The basin was divided into 27 sub basins comprising 193 hydrological response units. Monthly calibration and validation were performed using the measured discharge data of the Kajang station. One-at-a-time sensitivity analysis using Sequential Uncertainty Fitting (SUFI-2) algorithms was performed to examine the critical input variables of the study area. It was found that the SWAT model can be successfully applied for hydrological evaluation of the basin and the SCS runoff curve number, base flow alpha factor and groundwater delay were found to be the most sensitive parameters. The next step should be conducting a 30 years continuous hydrological modeling. It is needed to analyze the water balance and the hydrological trends of the basin due to the basin experienced major land used changes since 1980 for urbanization activities. IndexTerms-One-at-a-time sensitivity analysis, hydrological modeling, langat river basin, SUFI-2 algorithms.

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An ANFIS-based approach for predicting the bed load for moderately sized rivers

Cited by 82 publications

References 13 publications

Utilization of Fuzzy Computing in Agrophysics

Utilization of Fuzzy Computing in Agrophysics

Estimation of Some Coal Parameters Depending on Petrographic and Inorganic Analyses by Using Genetic Algorithm and Adaptive Neuro-Fuzzy Inference Systems

Application on One-at-a-Time Sensitivity Analysis of Semi-Distributed Hydrological Model in Tropical Watershed

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