Application of a new fuzzy logic model known as "SMRGT" for estimating flow coefficient rate

Ayşe Yeter Günal; Ruya Mehdi

Makale detayları

Application of a new fuzzy logic model known as "SMRGT" for estimating flow coefficient rate

Yazarlar:

Ayşe Yeter Günal | Ruya Mehdi

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Engineering

Dergi:

Turkish Journal of Engineering Vol.8 Issue 1, pp.46-55

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001298611

pISSN:

2587-1366

Anahtar kelimeler:

Özet:

 Since we all have our own set of limitations when it comes to perceiving the world and reasoning profoundly, we are constantly met with uncertainty as a result of a lack of information lexical impression, incompleteness, as well as specific measurement inaccuracies. It has been found that uncertainty, which shows up as ambiguity, is the root cause of complexity, which is everywhere in the real world. Most of the uncertainty in civil engineering systems comes from the fact that the constraints parameters are hard to understand and are described in a vague way. The ambiguity comes from a number of sources, including physical arbitrariness, statistical uncertainty due to using limited information to estimate these characteristics, and model uncertainty due to using overly simplified methods and idealized depictions of actual performances. Thus, it is better to combine fuzzy set theory and fuzzy logic. Fuzzy logic is well-suited to modelling the indeterminacy and ambiguity that results from multiple factors and a lack of data. In order to improve upon a previous predictive model, this paper uses a smart model built on a fuzzy logic system FLS. Precipitation, temperature, humidity, slope, and land use data were all taken into account as input variables in the fuzzy model. Topraks original explanation of the simple membership function and fuzzy rules generation technique SMRGT was based on the fuzzy-Mamdani methodology and used the flow coefficient as its output. The models results were compared to available data. The following factors were considered in the comparison: 1 The maximum, minimum, mean, standard deviation, skewness, variation, and correlation coefficients are the seven statistical parameters. 2 Four types of error criteria: Mean Absolute Relative Error MARE, Mean Square Error MSE, Mean Absolute Error MAE, and Root Mean Square Error RMSE. 3 Scatter diagram.

 Since we all have our own set of limitations when it comes to perceiving the world and reasoning profoundly, we are constantly met with uncertainty as a result of a lack of information lexical impression, incompleteness, as well as specific measurement inaccuracies. It has been found that uncertainty, which shows up as ambiguity, is the root cause of complexity, which is everywhere in the real world. Most of the uncertainty in civil engineering systems comes from the fact that the constraints parameters are hard to understand and are described in a vague way. The ambiguity comes from a number of sources, including physical arbitrariness, statistical uncertainty due to using limited information to estimate these characteristics, and model uncertainty due to using overly simplified methods and idealized depictions of actual performances. Thus, it is better to combine fuzzy set theory and fuzzy logic. Fuzzy logic is well-suited to modelling the indeterminacy and ambiguity that results from multiple factors and a lack of data. In order to improve upon a previous predictive model, this paper uses a smart model built on a fuzzy logic system FLS. Precipitation, temperature, humidity, slope, and land use data were all taken into account as input variables in the fuzzy model. Topraks original explanation of the simple membership function and fuzzy rules generation technique SMRGT was based on the fuzzy-Mamdani methodology and used the flow coefficient as its output. The models results were compared to available data. The following factors were considered in the comparison: 1 The maximum, minimum, mean, standard deviation, skewness, variation, and correlation coefficients are the seven statistical parameters. 2 Four types of error criteria: Mean Absolute Relative Error MARE, Mean Square Error MSE, Mean Absolute Error MAE, and Root Mean Square Error RMSE. 3 Scatter diagram.

 Since we all have our own set of limitations when it comes to perceiving the world and reasoning profoundly, we are constantly met with uncertainty as a result of a lack of information lexical impression, incompleteness, as well as specific measurement inaccuracies. It has been found that uncertainty, which shows up as ambiguity, is the root cause of complexity, which is everywhere in the real world. Most of the uncertainty in civil engineering systems comes from the fact that the constraints parameters are hard to understand and are described in a vague way. The ambiguity comes from a number of sources, including physical arbitrariness, statistical uncertainty due to using limited information to estimate these characteristics, and model uncertainty due to using overly simplified methods and idealized depictions of actual performances. Thus, it is better to combine fuzzy set theory and fuzzy logic. Fuzzy logic is well-suited to modelling the indeterminacy and ambiguity that results from multiple factors and a lack of data. In order to improve upon a previous predictive model, this paper uses a smart model built on a fuzzy logic system FLS. Precipitation, temperature, humidity, slope, and land use data were all taken into account as input variables in the fuzzy model. Topraks original explanation of the simple membership function and fuzzy rules generation technique SMRGT was based on the fuzzy-Mamdani methodology and used the flow coefficient as its output. The models results were compared to available data. The following factors were considered in the comparison: 1 The maximum, minimum, mean, standard deviation, skewness, variation, and correlation coefficients are the seven statistical parameters. 2 Four types of error criteria: Mean Absolute Relative Error MARE, Mean Square Error MSE, Mean Absolute Error MAE, and Root Mean Square Error RMSE. 3 Scatter diagram.

 Since we all have our own set of limitations when it comes to perceiving the world and reasoning profoundly, we are constantly met with uncertainty as a result of a lack of information lexical impression, incompleteness, as well as specific measurement inaccuracies. It has been found that uncertainty, which shows up as ambiguity, is the root cause of complexity, which is everywhere in the real world. Most of the uncertainty in civil engineering systems comes from the fact that the constraints parameters are hard to understand and are described in a vague way. The ambiguity comes from a number of sources, including physical arbitrariness, statistical uncertainty due to using limited information to estimate these characteristics, and model uncertainty due to using overly simplified methods and idealized depictions of actual performances. Thus, it is better to combine fuzzy set theory and fuzzy logic. Fuzzy logic is well-suited to modelling the indeterminacy and ambiguity that results from multiple factors and a lack of data. In order to improve upon a previous predictive model, this paper uses a smart model built on a fuzzy logic system FLS. Precipitation, temperature, humidity, slope, and land use data were all taken into account as input variables in the fuzzy model. Topraks original explanation of the simple membership function and fuzzy rules generation technique SMRGT was based on the fuzzy-Mamdani methodology and used the flow coefficient as its output. The models results were compared to available data. The following factors were considered in the comparison: 1 The maximum, minimum, mean, standard deviation, skewness, variation, and correlation coefficients are the seven statistical parameters. 2 Four types of error criteria: Mean Absolute Relative Error MARE, Mean Square Error MSE, Mean Absolute Error MAE, and Root Mean Square Error RMSE. 3 Scatter diagram.

Makale detayları

Application of a new fuzzy logic model known as "SMRGT" for estimating flow coefficient rate

Yazarlar:

Ayşe Yeter Günal | Ruya Mehdi

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Engineering

Dergi:

Turkish Journal of Engineering Vol.8 Issue 1, pp.46-55

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001298611

pISSN:

2587-1366

Fuzzy system

SMRGT

Flow Coefficient

Mamdani theory

Ambiguity

Sayı

Makale detayları

Application of a new fuzzy logic model known as "SMRGT" for estimating flow coefficient rate

Yazarlar: Ayşe Yeter Günal | Ruya Mehdi

Yayıncı: Murat YAKAR

Ana Başlıklar: Engineering

Dergi: Turkish Journal of Engineering Vol.8 Issue 1, pp.46-55

Belge Tipi: Makale

Yayın Tarihi: 1/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001298611

pISSN: 2587-1366

Fuzzy system

SMRGT

Flow Coefficient

Mamdani theory

Ambiguity

Sayı

Yazarlar:

Ayşe Yeter Günal | Ruya Mehdi

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Engineering

Dergi:

Turkish Journal of Engineering Vol.8 Issue 1, pp.46-55

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001298611

pISSN:

2587-1366