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Register a new userPrediction of Monthly Reference Evapotranspiration Using Artificial Neural Networks and Fuzzy Inference System
التنبّؤ بالتبخّر نتح المرجعي الشّهري باستخدام الشبكات العصبيّة الاصطناعية و نظام الاستدلال الضبابي
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يعتبر التبخر- نتح أحد المكونات الهامة في الدورة الهيدرولوجية، و تعد القدرة على التنبؤ الدقيق بقيم هذه الظاهرة من العوامل الهامة في العديد من تطبيقات الموارد المائية. تهدف هذه الدراسة إلى التنبؤ بقيم التبخر نتح المرجعي الشهري, باستخدام الشبكات العصبية الاصطناعية و نظام الاستدلال الضبابي.
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).
Artificial intelligence review:
Research summary
تتناول هذه الدراسة موضوع التنبؤ بقيم التبخر-نتح المرجعي الشهري باستخدام الشبكات العصبية الاصطناعية ونظام الاستدلال الضبابي. يعتبر التبخر-نتح أحد المكونات الهامة في الدورة الهيدرولوجية، وله أهمية كبيرة في إدارة الموارد المائية. اعتمدت النماذج المستخدمة في الدراسة على القيم الشهرية لدرجة حرارة الهواء العظمى والصغرى والرطوبة النسبية كمدخلات، بينما كانت المخرجات هي قيم التبخر-نتح المرجعي الشهري المحسوبة باستخدام طريقة بنمان-مونتيث. أظهرت النتائج موثوقية عالية لكل من الشبكات العصبية الاصطناعية ونظام الاستدلال الضبابي دون وجود أفضلية واضحة لأحدهما، حيث كانت قيمة جذر متوسط مربع الخطأ خلال مرحلة الاختبار متساوية لكل منهما. توصي الدراسة باستخدام أساليب الذكاء الاصطناعي في التنبؤ بالظواهر الهيدرولوجية والعمليات المتعلقة بالموارد المائية.
Critical review
دراسة نقدية: تعتبر هذه الدراسة خطوة مهمة في مجال استخدام الذكاء الاصطناعي لتنبؤ الظواهر الهيدرولوجية، إلا أنها تفتقر إلى تحليل أعمق لتأثير العوامل المناخية الأخرى مثل سرعة الرياح والإشعاع الشمسي، والتي قد تكون لها تأثير كبير على دقة التنبؤ. كما أن الدراسة لم تتناول بشكل كافٍ كيفية تحسين النماذج المستخدمة أو دمجها مع نماذج أخرى للحصول على نتائج أكثر دقة. بالإضافة إلى ذلك، كان من الممكن أن تكون الدراسة أكثر شمولية إذا تم اختبار النماذج على مجموعات بيانات من مناطق مختلفة لتقييم مدى عموميتها.
Questions related to the research
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ما هي المدخلات المستخدمة في نماذج التنبؤ في هذه الدراسة؟
المدخلات المستخدمة هي القيم الشهرية لدرجة حرارة الهواء العظمى والصغرى والرطوبة النسبية.
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ما هي طريقة الحساب المستخدمة لقيم التبخر-نتح المرجعي الشهري في الدراسة؟
تم حساب قيم التبخر-نتح المرجعي الشهري باستخدام طريقة بنمان-مونتيث.
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هل أظهرت الدراسة أفضلية واضحة لأحد النماذج المستخدمة؟
لا، أظهرت الدراسة موثوقية عالية لكل من الشبكات العصبية الاصطناعية ونظام الاستدلال الضبابي دون وجود أفضلية واضحة لأحدهما.
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ما هي التوصيات التي قدمتها الدراسة؟
توصي الدراسة باستخدام أساليب الذكاء الاصطناعي في التنبؤ بالظواهر الهيدرولوجية والعمليات المتعلقة بالموارد المائية.
References used
AL-ABBODI, A. H. 2014. Evaporation Estimation Using Adaptive Neuro-Fuzzy Inference System and Linear Regression. Eng. &Tech. Journal, Vol.32, Part(A), No.10, 2465-2474
JADEJA, V, 2011. Artificial neural network estimation of Reference Evapotranspiration from pan evaporation in a semiarid environment. National Conference on Recent Trends in Engineering & Technology
KARIYAMA, I. D, 2014. Temperature-Based Feed-Forward Backpropagation Artificial Neurl Network For Estimation Reference Crop Evapotranspiration In The Upper West Region. International Journal of Scientific & Technology Research, Volume 3, Issue 8, 357-364
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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.
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.
Evapotranspiration forms one of the hydrology cycle elements that it's hard to
measure its actual amounts in the field conditions, so it’s estimated by calculations of
experimental relations that depend on climatic elements data. These estimations
include
different errors because of approximation processes. The research goals to accurate
estimation of the monthly reference evapotranspiration amount in Safita area (on the east
coast of the Mediterranean Sea), and the research depends on the technique of Artificial
Neural Network (ANN), and the mathematical model was built by the
(nftool), which is one of the Matlab tools, depending on monthly air temperature and
relative humidity data which were taken from Safita meteorological station, and the data of
monthly pan evaporation (Class A pan) has been used, after modifying its results, for the
purpose of checking the performance accuracy of the network, by using Simulink
technique, which is existing in Matlab Programs Package. The results of the research
verify that a multi-layer ANN of error Back-propagation algorithm gives a good result in
estimating monthly reference Evapo-transpiration for the used data group.
This research aims to produce a diagnosis system for breast cancer by using Neural
Network depending on Back Propagation algorithm(BPNN) and Adaptive Neuro Fuzzy
Inference System ‘ANFIS’, the both of studies was done using structural features of
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iopsies in “Wisconson Breast Cancer “data base.
In the end a comparison was made between the two studies of malignant- benign
classification of breast masses of breast cancer which has accuracy 95,95% with BPNN
and 91.9% with ANFIS system, this results can be consider very important if they
compared with researches depending on image features that obtained of various devises
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proposed model. Controller has shown excelled performance in terms of reducing
displacements, velocity and acceleration.
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