Do you want to publish a course? Click here

Prediction of Monthly Pan Evaporation in Homs Meteostation Using Artificial Neural Networks

التنبّؤ بالتبخّر الإنائي الشّهري في محطّة حمص المناخيّة باستخدام الشبكات العصبيّة الاصطناعيّة

1249   0   50   0 ( 0 )
 Publication date 2016
and research's language is العربية
 Created by Shamra Editor




Ask ChatGPT about the research

Evaporation is a major meteorological component of the hydrologic cycle, and it plays an influential role in the development and management of water resources. The aim of this study is to predict of the monthly pan evaporation in Homs meteostation using Artificial Neural Networks (ANNs), which based on monthly air temperature and relative humidity data only as inputs, and monthly pan evaporation as output of the network. The network was trained and verified using a back-propagation algorithm with different learning methods, number of processing elements in the hidden layer(s), and the number of hidden layers. Results shown good ability of (2-10-1) ANN to predict of monthly pan evaporation with total correlation coefficient equals 96.786 % and root mean square error equals 24.52 mm/month for the total data set. This study recommends using the artificial neural networks approach to identify the most effective parameters to predict evaporation.


Artificial intelligence review:
Research summary
تتناول هذه الدراسة التنبؤ بالتبخر الإنائي الشهري في محطة حمص المناخية باستخدام الشبكات العصبية الاصطناعية. يعتبر التبخر مكونًا أساسيًا في الدورة الهيدرولوجية وله دور كبير في إدارة الموارد المائية. استخدمت الدراسة القيم الشهرية لدرجة حرارة الهواء والرطوبة النسبية كمدخلات، والتبخر الإنائي الشهري كمخرج للشبكة العصبية. تم تدريب الشبكة باستخدام خوارزمية الانتشار العكسي مع تغيير طرق التدريب وعدد الطبقات الخفية وعدد العصبونات في كل طبقة. أظهرت النتائج أن الشبكة العصبية ذات الهيكلية 1-10-2 قادرة على التنبؤ بقيم التبخر الإنائي الشهري بمعامل ارتباط كلي 96.786% وجذر متوسط مربعات الأخطاء 24.52 ملم/شهر. توصي الدراسة باستخدام الشبكات العصبية الاصطناعية لتحديد العوامل الأكثر تأثيرًا على التبخر.
Critical review
دراسة نقدية: تعتبر هذه الدراسة خطوة مهمة نحو تحسين دقة التنبؤ بالتبخر الإنائي باستخدام الشبكات العصبية الاصطناعية. ومع ذلك، يمكن توجيه بعض النقد البناء لتحسين العمل المستقبلي. أولاً، قد يكون من المفيد تضمين المزيد من العوامل المناخية مثل سرعة الرياح والإشعاع الشمسي لتحسين دقة النموذج. ثانيًا، يمكن توسيع الدراسة لتشمل محطات مناخية أخرى في سوريا أو حتى في مناطق جغرافية مختلفة للحصول على نتائج أكثر شمولية. أخيرًا، يمكن مقارنة أداء الشبكات العصبية مع نماذج أخرى مثل نماذج التعلم الآلي التقليدية أو النماذج الفيزيائية لتحسين فهم أداء الشبكات العصبية في هذا السياق.
Questions related to the research
  1. ما هي المدخلات والمخرجات المستخدمة في الشبكة العصبية الاصطناعية في هذه الدراسة؟

    استخدمت الدراسة القيم الشهرية لدرجة حرارة الهواء والرطوبة النسبية كمدخلات، والتبخر الإنائي الشهري كمخرج للشبكة العصبية.

  2. ما هي خوارزمية التدريب المستخدمة في هذه الدراسة؟

    استخدمت الدراسة خوارزمية الانتشار العكسي لتدريب الشبكة العصبية الاصطناعية.

  3. ما هو معامل الارتباط الكلي الذي حققته الشبكة العصبية الاصطناعية في التنبؤ بالتبخر الإنائي الشهري؟

    حققت الشبكة العصبية الاصطناعية معامل ارتباط كلي بلغ 96.786%.

  4. ما هي التوصيات التي قدمتها الدراسة لتحسين دقة التنبؤ بالتبخر الإنائي؟

    أوصت الدراسة باستخدام تقنية الشبكات العصبية الاصطناعية لتحديد العوامل الأكثر تأثيرًا على التبخر لتحسين دقة التنبؤ.


References used
ESLAMIAN, S. S; GOHARI, S. A; BIABANKI, M; MALEKIAN, R; Estimation of Monthly Pan Evaporation Using Artificial Neural Networks and Support Vector Machines. Journal of Applied Sciences 8 ,19, 2008, 3497-3502
BOROOMAND-NASAB, B; JOORABIAN, M. Estimating Monthly Evaporation Using Artificial Neural Networks. Journal of Environmental Science and Engineering, 5, 2011, 88-91
KUMAR, P; TIWARI, A. K. Evaporation Estimation Using Artificial Neural Network. International Journal of Computer Theory and Engineering, Vol. 4, No. 1, 2012
rate research

Read More

Accurate estimating and predicting of hydrological phenomena plays an influential role in the development and management of water resources, preparing of future plans according to different scenarios of climate changes. Evapotranspiration is one of t he major meteorological components of the hydrologic cycle and from the most complex of them, and the accurate prediction of this parameter is very important for many water resources applications. So, this research goals to prediction of monthly reference evapotranspiration (ET0) at Homs meteostation, in the middle of Syrian Arab Republic, using Artificial Neural Networks (ANNs), and Fuzzy Inference System (FIS), depending on available climatic data, and comparision between the results of these models. The used data contained 347 monthly values of Air Temperature (T), Relative Humidity (RH), Wind Speed (WS) and Sunshine Hours (SS) (from October 1974 to December 2004). The monthly reference evapotranspiration data were estimated by the Penman Monteith method, which is the proposed method by Food and Agriculture Organization of the United Nations (FAO) as the standard method for the estimation of ET0, and used as outputs of the models. The results of this study showed that feed forward back propagation Artificial Neural Networks (FFBP-ANNs) pridected successfully the monthly ET0 using climatic data, with low values of root mean square errors (RMSE), and high values of correlation coefficients (R), and showed that the using of the monthly index as an additional input, improves the accurate of prediction of the artificial neural networks models. Also, the results showed good ability of Fuzzy Inference Models (FIS) in predicting of monthly reference evapotranspiration. Sunshine hours are the most influential single parameter for ET0 prediction (R= 97.71%, RMSE = 18.08 mm/month) during the test period, sunshine hours and wind speed are the most influential optimal combination of two parameters (R= 98.55%, RMSE = 12.49 mm/month) during the test period. The results showed high reliability for each of the artificial neural networks and fuzzy inference system with a little preference for artificial neural networks which can add the monthly index in the input layer, and there for improve the presicion of predictions. This study recommends the using of artificial intelligence techniques in modeling of complex and nonlinear phenomena which related of water resources.
Evapotranspiration is an important component of the hydrologic cycle, and the accurate prediction of this parameter is very important for many water resources applications. Thus, the aim of this study is prediction of monthly reference evapotranspiration using Artificial Neural Networks (ANNs) and fuzzy inference system (FIS).
The evaporation is one of the basic components of the hydrologic cycle and it is essential for studies such as water balance, irrigation system design and water resource management, and it requires knowledge of many climatic variables. Although, th ere are many empirical formulas available for evaporation estimate, but their performances are not all satisfactory due to the complicated nature of the evaporation process. Accordingly, this paper is an attempt to assess the potential and usefulness of ANN based modeling for evaporation prediction from HAMA by using temperature, relative humidity and wind velocity. The mathematical model was built by the (nntool-box), which is one of the MATLAB tools. The feed forward back propagation network with one hidden layer has been utilised to construct the model. Different networks with different number of neurons were evaluated. Root Mean Squared Error (RMSE) was employed to evaluate the accuracy of the proposed model. The study shows that ANN (3-14-1) was the best model with RMSE (21.5mm/month) and R2 (0.97). This study suggests using other types of neural networks for estimation of evaporation
Weather forecasting (especially rainfall) is one of the most important and challenging operational tasks carried out by meteorological services all over the world. Itis furthermore a complicated procedure that requires multiple specialized fields o f expertise. In this paper, a model based on artificial neural networks (ANNs) and wavelet Transform is proposed as tool to predict consecutive monthly rainfalls (1933-2009) taken of Homs Meteorological Station on accounts of the preceding events of rainfall data. The feed-forward neural network with back-propagation Algorithm is used in the learning and forecasting, where the time series of rain that detailed transactions and the approximate three levels of analysis using a Discrete wavelet transform (DWT). The study found that the neural network WNN structured )5-8-8-8-1(, able to predict the monthly rainfall in Homs station on the long-term correlation of determination and root mean squared-errors (0.98, 7.74mm), respectively. Wavelet Transform technique provides a useful feature based on the analysis of the data, which improves the performance of the model and applied this technique in ANNmodels for rain because it is simple, as this technique can be applied to other models.
This paper presents a new technique based on artificial neural networks (ANNs) to correct power factor. A synchronous motor controlled by the neural controller was used to handle the problem of reactive power compensation of the system, in order to correct power factor. In this paper, the electrical system and the neural controller were simulated using MATLAB. The results have shown that the presented technique overcomes the problems in conventional compensators (using static capacitors) such as time delay and step changes of reactive power besides to the fast compensation compared to the technique with capacitors groups.
comments
Fetching comments Fetching comments
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا