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

Demand and Capacity Modelling for Acute Services using Discrete Event Simulation

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Health Systems following peer review. The final publication [Demir, E., Gunal, M & Southern, D., Health Syst (2016), first published online March 11, 2016, is available at Springer via http://dx.doi.org/doi:10.1057/hs.2016.1 © 2016 Operational Research Society Ltd 2016Increasing demand for services in England with limited healthcare budget has put hospitals under immense pressure. Given that almost all National Health Service (NHS) hospitals have severe capacity constraints (beds and staff shortages) a decision support tool (DST) is developed for the management of a major NHS Trust in England. Acute activities are forecasted over a 5 year period broken down by age groups for 10 specialty areas. Our statistical models have produced forecast accuracies in the region of 90%. We then developed a discrete event simulation model capturing individual patient pathways until discharge (in A&E, inpatient and outpatients), where arrivals are based on the forecasted activity outputting key performance metrics over a period of time, e.g., future activity, bed occupancy rates, required bed capacity, theatre utilisations for electives and non-electives, clinic utilisations, and diagnostic/treatment procedures. The DST allows Trusts to compare key performance metrics for 1,000’s of different scenarios against their existing service (baseline). The power of DST is that hospital decision makers can make better decisions using the simulation model with plausible assumptions which are supported by statistically validated data.Peer reviewedFinal Accepted Versio

Exploring alternative routes to realising the benefits of simulation in healthcare

Discrete event simulation should offer numerous benefits in designing healthcare systems but the reality is often problematic. Healthcare modelling faces particular challenges: genuine, fundamental variations in practice and an opposition to any suggestion of standardisation from some professional groups. This paper compares the experiences of developing a new simulation in an Accident and Emergency (A&E) Department, a subsequent adaptation for modelling an outpatient clinic and applications of a generic A&E simulation. These studies provide examples of three distinct approaches to realising the potential benefits of simulation: the bespoke, the reuse and the generic route. Reuse has many advantages: it is relatively efficient in exploiting previous modelling experience, delivering timely results while providing scope for adaptations to local practice. Explicitly demonstrating this willingness to adapt to local conditions and engaging with stakeholders is particularly important in healthcare simulation

A novel healthcare resource allocation decision support tool: A forecasting-simulation-optimization approach

© 2020 Informa UK Limited, trading as Taylor & Francis Group. This is an accepted manuscript of an article published by Taylor & Francis in Journal of the Operational Research Society on 03 Feb 2020, available online: https://doi.org/10.1080/01605682.2019.1700186.The increasing pressures on the healthcare system in the UK are well documented. The solution lies in making best use of existing resources (e.g. beds), as additional funding is not available. Increasing demand and capacity shortages are experienced across all specialties and services in hospitals. Modelling at this level of detail is a necessity, as all the services are interconnected, and cannot be assumed to be independent of each other. Our review of the literature revealed two facts; First an entire hospital model is rare, and second, use of multiple OR techniques are applied more frequently in recent years. Hybrid models which combine forecasting, simulation and optimization are becoming more popular. We developed a model that linked each and every service and specialty including A&E, and outpatient and inpatient services, with the aim of, (1) forecasting demand for all the specialties, (2) capturing all the uncertainties of patient pathway within a hospital setting using discrete event simulation, and (3) developing a linear optimization model to estimate the required bed capacity and staff needs of a mid-size hospital in England (using essential outputs from simulation). These results will bring a different perspective to key decision makers with a decision support tool for short and long term strategic planning to make rational and realistic plans, and highlight the benefits of hybrid models.Peer reviewe

The Rare Coincidence: Nonrecurrent Laryngeal Nerve Pointed by a Zuckerkandl's Tubercle

The safety of thyroid operations mainly depends on complete anatomical knowledge. Anatomical and embryological variations of the inferior laryngeal nerve (ILN), of the thyroid gland itself and unusual relations between ILN and the gland threaten operation security are discussed. The patient with toxic multinodular goiter is treated with total thyroidectomy. During dissection of the right lobe, the right ILN which has nonrecurrent course arising directly from cervical vagus nerve is identified and fully isolated until its laryngeal entry. At the operation, we observe bilateral Zuckerkandl's tubercles (ZTs) as posterior extension of both lateral lobes. The left ILN has usual recurrent course in the trachea-esophageal groove. The right ZT is placed between upper and middle third of the lobe points the nonrecurrent ILN. The coincidence of non-recurrent ILN pointed by a ZT is rare anatomical and embryological feature of this case. Based on anatomical and embryological variations, we suggest identification and full exposure of ILN before attempting excision of adjacent structures, like the ZT which has surgical importance for completeness of thyroidectomy

Traumatic pulmonary pseuodocysts: two case reports

Traumatic pulmonary pseudocyst (TPP) is a rare complication, sometimes encountered after blunt thoracic trauma and even more rarely following penetrating injuries. It is more common among pediatric and young adult patients. Although TPP is usually benign in nature, complications associated with hemoptysis and secondary infection may develop. The treatment is conservative. In this report, we present two rare cases of TPP occuring after a high-speed accident and a stab wound injury, where conservative treatment provided good outcomes

Success and Failure in the Simulation of an Accident and Emergency Department

Healthcare simulation has the potential to offer many benefits but the implementation is often problematic. This paper describes the development of a simulation of an Accident and Emergency Department in an NHS hospital. The early experience of the client provoked great enthusiasm but ultimately the simulation failed to meet all expectations. The simulation delivered a number of benefits, notably in terms of stimulating constructive debate and helping the stakeholders appreciate the complete Accident and Emergency system. The project produced a technically proficient tool that was delivered too late to have the desired impact. This mixed record of success appears typical of many simulations. Important lessons were learned, both technically and in the management of client expectations, which have contributed to subsequent successful implementation in other departments of the hospital. The experience suggests that both potential clients and analysts need to establish realistic expectations and appreciate the particular challenges of simulation in a healthcare environment