Ruya mehdi zainalabdeen -

Gender	FEMALE
Place of Work	Technical Engineering College/ Kirkuk
Position	Lecturer
Qualification	Ph.d
Speciality	Civil engineering
Email	ruya.mehdi25@ntu.edu.iq
Phone	07754766096
Address	jamiya street, kirkuk, kirkuk, Iraq

The impact of land use and slope change in flow coefficient estimation

Oct 24, 2023

Journal Engineering Applications

publisher Mersin university

Issue 2

Volume 3

The prediction of floods, which are widely recognized as one of the most devastating hazards on our planet, poses significant challenges primarily stemming from the absence of a dependable forecasting model. Following seismic events, floods rank as the costliest natural calamity in Turkey. The mitigation of existing challenges can be significantly enhanced through the utilization of flow coefficient calculations, which serve as the foremost determinant of flood flow dynamics. The extant body of literature encompasses a diverse range of methodologies for modelling flow coefficients. However, the majority of these methods depend on black-box techniques that lack transferability. The selection of the fuzzy SMRGT Method for this investigation was based on its consideration of the underlying physics of the event, making it a novel approach. The land use and slope data of the Aksu river basin were utilized. The outcomes generated by the model were compared to the empirical data. The evaluation of the model's performance encompassed various metrics, such as root mean square error, mean absolute error, mean absolute relative error, and coefficient of determination. The findings indicated that the fuzzy inference system that was proposed exhibited a high level of predictive accuracy, as evidenced by an overall coefficient of determination (R2) of 0.998.

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Computation of Flow Coefficient via Non-deterministic Approach of Fuzzy Logic Called "SMRGT" Based on Meteorological Properties

Oct 5, 2023

Journal Jordan Journal of Civil Engineering

publisher Jordan University of Science and Technology

DOI https://doi.org/10.14525/JJCE.v17i4.11

Issue 4

Volume 17

In light of the current global climate changes, floods have emerged as a significant hydraulic and hydrological challenge on a global scale. The primary contributors to the expansion of impermeable areas and the intensification of flood flow are extensive urbanization, the proliferation of concrete edifices and the construction of asphalt thoroughfares. Anticipating the flow beforehand will be conducive to the successful execution of the task at hand. The objective is to reduce the likelihood of harm to individuals and damage to assets. By accurately determining the flow coefficient, which is a significant factor in flood flow, it is possible to mitigate existing issues to a significant degree. Numerous methodologies for modeling flow coefficients can be found in the extant literature. However, most of these methodologies rely on black-box techniques and are not easily generalizable. Hence, the present investigation has opted for a novel methodology; namely, the fuzzy SMRGT method that takes into account the physical characteristics of the phenomenon and is designed to assist individuals who encounter difficulties in selecting the appropriate quantity, structure and rationale of membership functions and fuzzy rules within a given fuzzy set. The data comprising annual precipitation, temperature and relative humidity measurements was acquired from the Regional Directorate of Meteorology. The model outcomes were juxtaposed with the actual observations. Statistical parameters, such as the coefficient of determination (R²), the root mean square error (RMSE), the Nash-Sutcliffe efficiency coefficient (NSE), and the mean absolute percentage error (MAPE), were used to evaluate the performance of the model. The statistical test results were RMSE: 0.096, NSE: 0.90, MAPE: 17.3, and R²: 0.96. The findings suggest that the SMRGT model is highly effective in accurately forecasting the flow coefficient and represents a robust approach for constructing membership functions and fuzzy rules.

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

Sep 15, 2023

Journal Turkish Journal of Engineering

publisher dergipark

DOI 10.31127/tuje.1225795

Issue 8

Volume 1

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. Toprak's 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 model's 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.

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Flow Coefficient Determination in Catchment Based on Analysis of Temperature and Wind Speed Data Using the Fuzzy SMRGT Method

Aug 25, 2023

Journal IOP Conference Series: Earth and Environmental Science

publisher IOP publishing

DOI 10.1088/1755-1315/1222/1/012014

Issue 1222

In engineering hydrology, calculating the flow coefficient is a crucial step. The flow coefficient calculation is necessary for directing the rational profiteering of water resources, improving the overall efficiency of water resource utilization, and minimizing the effect of catastrophic events. By precisely determining the flow coefficient, which is the most influential factor in flood flow, the current issues will be mitigated substantially. Various techniques are available in the existing literature for modelling flow coefficient. Most of them, however, rely on black-box approaches that are not generalizable. Therefore, this paper applied an intelligent model based on a fuzzy logic system called the Simple Membership Function and Fuzzy Rules Generation Technique (SMRGT). The new technique considers the physical cause-effect relationship and is intended to aid individuals who struggle to choose the number, form, and logic of membership functions and fuzzy rules in any fuzzy set. The study area's temperature and wind speed data were incorporated into the SMRGT model's input variables. The output was the flow coefficient. The prediction made by the model was validated against observational data. The comparison relies on numerous statistics and errors. The results indicated that the SMRGT model predicts the flow coefficient extraordinarily well and is an excellent method for generating membership functions and fuzzy rules.

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Prediction of Runoff Coefficient under Effect of Climate Change Using Adaptive Neuro Inference System

Jun 25, 2023

Journal journal of university of Babylon for engineering science

publisher University of Babylon

Issue 4

Volume 31

The complex characteristics of the rainfall- runoff mechanism, along with its non linear attributes and inherent uncertainties, have prompted scholars to explore alternative approaches inspired by natural phenomena. In order to tackle these obstacles, artificial neural networks (ANN) and fuzzy systems (FL) have been utilised as feasible substitutes for conventional physical models. Furthermore, the procurement of comprehensive data is considered essential for precise analysis and modelling. This study's primary objective was to use pertinent climatic data such as; Precipitation (P), Temperature (T), Relative humidity (Rh), and Wind speed (Ws) to predict the runoff coefficient using the Adaptive Neuro-Fuzzy Inference System (ANFIS) model. Different ranges (60:40; 70:30; 80:20) were used for the training and testing phases. The model was employed to predict the runoff coefficient in the Aksu river basin in Antalya province in Turkey. The study conducted a comparative analysis of the results, taking into account various performance indicators of the model, such as mean absolute error (MAE), Nash-Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE), and correlation coefficient (R2). Based on the findings presented, the (60:40) range showed the best results as evidenced by its low RMSE and MAE values and its high R2 and NSE values (RMSE:0.056, MAE:1.92, NSE:0.868, R2 :0.996). It was concluded that the ANFIS model magnificently predicts runoff coefficients with an exceptional level of precision, also the study findings indicate that accurate runoff coefficient estimation can be achieved using meteorological data without incorporating more intricate and interrelated data.

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Forecasting the Flow Coefficient of the River Basin Using Adaptive Fuzzy Inference System (ANFIS), and Fuzzy SMRGT method

May 24, 2023

Journal Journal of Ecological Engineering

publisher Lublin University of Technology

DOI 10.12911/22998993/163367

Issue 7

Volume 24

In hydrology and water resources engineering, predicting the flow coefficient is a crucial task that helps estimate the precipitation resulting in a surface flow. Accurate flow coefficient prediction is essential for efficient water management, flood control strategy development, and water resource planning. This investigation calculated the flow coefficient using models based on Simple Membership functions and fuzzy Rules Generation Technique (SMRGT) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The fuzzy logic methods are used to model the intricate connections between the inputs and the output. Statistical parameters such as the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) were used to evaluate the performance of models. The outcomes demonstrated that the physics-based model (SMRGT) could predict the flow coefficient more accurately and reliably than ANFIS because it allows for incorporating expert knowledge and domain-specific information, making it a suitable approach for a wide variety of problems

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A COMPARATIVE STUDY OF USING ADAPTIVE NEURAL FUZZY INFERENCE SYSTEM (ANFIS), GAUSSIAN PROCESS REGRESSION (GPR), AND SMRGT MODELS IN FLOW COEFFICIENT ESTIMATION.

May 23, 2023

Journal 3C Tecnología

publisher 3ciencias

DOI 10.17993/3ctecno.2023.v12n2e44.125-146

Issue 2

Volume 12

Estimating the flow coefficient is a crucial hydrologic process that plays a significant role in flood forecasting, water resource planning, and flood control. Accurate prediction of the flow coefficient is essential to prevent flood-related losses, manage flood warning systems, and control water flow. This study aimed to predict the flow coefficient for a period of 19 years (2000-2019) in the Aksu River Sub-Basin in Turkey, using historical climatic data, including precipitation, temperature, and humidity, provided by The Turkish State of Meteorological Service (TSMS). The study utilized three different approaches, namely, the Adaptive Neural Fuzzy Inference System (ANFIS), Simple Membership function and fuzzy Rules Generation Technique (SMRGT), and Gaussian Process Regression (GPR), to predict the flow coefficient. The models were evaluated using several statistical tests, such as Root Mean Square Error (RMSE), Coefficient of Determination (R2), Mean Absolute Error (MAE), and Mean Square Error (MSE), to determine their accuracy. Based on the evaluation criteria, it is concluded that the Simple Membership Functions and Fuzzy Rules Generation Technique (SMRGT) model has superior flow coefficient estimation performance than the other models.

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The study of land use and slope role in flow coefficient determination.

Dec 3, 2022

Journal Advanced Engineering Days

publisher Mersin university

Issue 5th Advanced Engineering Days (AED) Symposium

Volume 5

Floods, one of the most destructive risks on the planet's surface, are difficult to forecast using an accurate model. In Turkey, floods are the second most costly natural disaster after earthquakes. By determining the flow coefficient, which is the most effective factor in flood flow, the existing problems will be greatly reduced. There are numerous techniques for modeling flow coefficients available in the existing literature. However, the majority of them are based on black-box methodologies that cannot be generalized. In this study, a new approach called the Fuzzy SMRGT Method, which takes into account the physics of the event, was chosen. The data containing the land use and slope details of the Aksu river basin were used. The model's output was compared to actual data.

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