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

دفعت الخصائص المعقدة لآلية جريان الأمطار ، جنبًا إلى جنب مع سماتها غير الخطية والشكوك المتأصلة ، العلماء إلى استكشاف مناهج بديلة مستوحاة من الظواهر الطبيعية. من أجل معالجة هذه العقبات ، تم استخدام الشبكات العصبية الاصطناعية (ANN) والأنظمة الضبابية (FL) كبدائل مجدية للنماذج الفيزيائية التقليدية. علاوة على ذلك ، يعتبر شراء البيانات الشاملة أمرًا ضروريًا للتحليل الدقيق والنمذجة. كان الهدف الأساسي لهذه الدراسة هو استخدام البيانات المناخية ذات الصلة مثل ؛ هطول الأمطار (P) ودرجة الحرارة (T) والرطوبة النسبية (Rh) وسرعة الرياح (Ws) للتنبؤ بمعامل الجريان السطحي باستخدام نموذج نظام الاستدلال العصبي الضبابي التكيفي (ANFIS). تم استخدام نطاقات مختلفة (60:40 ؛ 70:30 ؛ 80:20) لمرحلتي التدريب والاختبار. تم استخدام النموذج للتنبؤ بمعامل الجريان السطحي في حوض نهر أكسو في مقاطعة أنطاليا في تركيا. أجرت الدراسة تحليلاً مقارناً للنتائج ، مع مراعاة مؤشرات الأداء المختلفة للنموذج ، مثل متوسط ​​الخطأ المطلق (MAE) ، ومعامل كفاءة ناش-ساتكليف (NSE) ، وجذر متوسط ​​الخطأ التربيعي (RMSE) ، والارتباط. معامل (R2). بناءً على النتائج المقدمة ، أظهر النطاق (60:40) أفضل النتائج كما يتضح من قيم RMSE و MAE المنخفضة وقيم R2 و NSE العالية (RMSE: 0.056 ، MAE: 1.92 ، NSE: 0.868 ، R2 : 0.996). استنتج أن نموذج ANFIS يتنبأ بشكل رائع بمعاملات الجريان السطحي بمستوى استثنائي من الدقة ، كما تشير نتائج الدراسة إلى أنه يمكن تحقيق تقدير دقيق لمعامل الجريان السطحي باستخدام بيانات الأرصاد الجوية دون دمج بيانات أكثر تعقيدًا وترابطًا.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

A large choroid plexus cyst diagnosed with magnetic resonance imaging in utero: a case report

The incidence of choroid plexus cysts represents approximately 1% of fetal anomalies. We describe a case in which fetal ultrasonography and fetal magnetic resonance scans were used to identify a large choroid cyst in a fetus without the use of a diagnostic amniocentesis to detect aneuploidy. After birth, the child underwent surgery. In conclusion, the nature of prenatal intracranial cysts should be fully evaluated and differentiated between choroid plexus cysts and other types of cysts. We believe that a detailed evaluation of detected cysts and other structural brain abnormalities are essential. Prenatal magnetic resonance scans clearly can decrease the need for risky procedures, such as an amniocentesis, in the evaluation of antenatal choroid plexus cysts

Forecasting the Flow Coefficient of the River Basin Using Adaptive Fuzzy Inference System and Fuzzy SMRGT method

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 statistical tests outcome for the SMRGT model was (RMSE:0.056, MAE:1.92, MAPE:6.88, R2:0.996), and for the ANFIS was (RMSE:0.96, MAE:2.703, MAPE:19.97, R2:0.8038). According to the findings, the SMRGT, a physics-based model, exhibited superior accuracy and reliability in predicting the flow coefficient compared to ANFIS. This is attributed to the SMRGT's ability to integrate expert knowledge and domain-specific information, rendering it a viable solution for a diverse range of issues

Forecasting the Flow Coefficient of the River Basin Using Adaptive Fuzzy Inference System and Fuzzy SMRGT Method

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 statistical tests outcome for the SMRGT model was (RMSE:0.056, MAE:1.92, MAPE:6.88, R2:0.996), and for the ANFIS was (RMSE:0.96, MAE:2.703, MAPE:19.97, R2:0.8038). According to the findings, the SMRGT, a physics-based model, exhibited superior accuracy and reliability in predicting the flow coefficient compared to ANFIS. This is attributed to the SMRGT’s ability to integrate expert knowledge and domain-specific information, rendering it a viable solution for diverse issues