Estimation of daily pan evaporation using adaptive neural-based fuzzy inference system

2011 ◽  
Vol 1 (3/4) ◽  
pp. 164 ◽  
Author(s):  
Saeid Eslamian ◽  
Mohammad Javad Amiri
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Pan Evaporation ◽  
Inference System ◽  
Daily Pan Evaporation

Estimating daily pan evaporation from climatic data of the State of Illinois, USA using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN)

2011 ◽  
Vol 42 (6) ◽  
pp. 491-502 ◽  
Author(s):  
J. Shiri ◽  
W. Dierickx ◽  
A. Pour-Ali Baba ◽  
S. Neamati ◽  
M. A. Ghorbani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Pan Evaporation ◽  
Climatic Parameters ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Daily Pan Evaporation ◽  
Artificial Neural Network Ann

Evaporation is a major component of the hydrological cycle. It is an important aspect of water resource engineering and management, and in estimating the water budget of irrigation schemes. The current work presents the application of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches for modeling daily pan evaporation using daily climatic parameters. The neuro-fuzzy and neural network models are trained and tested using the data of three weather stations from different geographical positions in the U.S. State of Illinois. Daily meteorological variables such as air temperature, solar radiation, wind speed, relative humidity, surface soil temperature and total rainfall for three years (August 2005 to September 2008) were used for training and testing the employed models. Statistic parameters such as the coefficient of determination (R2), the root mean squared error (RMSE), the variance accounted for (VAF), the adjusted coefficient of efficiency (E1) and the adjusted index of agreement (d1) are used to evaluate the performance of the applied techniques. The results obtained show the feasibility of the ANFIS and ANN evaporation modeling from the available climatic parameters, especially when limited climatic parameters are used.

scholarly journals Using fuzzy logic for estimating monthly pan evaporation from meteorological data in Emara/ South of Iraq

2012 ◽  
Vol 9 (1) ◽  
pp. 133-140 ◽  
Author(s):  
Baghdad Science Journal
Keyword(s):  
Fuzzy Logic ◽  
Wind Speed ◽  
Relative Humidity ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Hydrological Cycle ◽  
Meteorological Data ◽  
Accurate Estimation ◽  
Pan Evaporation ◽  
Inference System

Evaporation is one of the major components of the hydrological cycle in the nature, thus its accurate estimation is so important in the planning and management of the irrigation practices and to assess water availability and requirements. The aim of this study is to investigate the ability of fuzzy inference system for estimating monthly pan evaporation form meteorological data. The study has been carried out depending on 261 monthly measurements of each of temperature (T), relative humidity (RH), and wind speed (W) which have been available in Emara meteorological station, southern Iraq. Three different fuzzy models comprising various combinations of monthly climatic variables (temperature, wind speed, and relative humidity) were developed to evaluate effect of each of these variables on estimation process. Two error statistics namely root mean squared error and coefficient of determination were used to measure the performance of the developed models. The results indicated that the model, whose input variables are T, W, and RH, perform the best for estimating evaporation values. In addition, the model which is dominated by (T) is significantly and distinctly helps to prove the predictive ability of fuzzy inference system. Furthermore, agreements of the results with the observed measurements indicate that fuzzy logic is adequate intelligent approach for modeling the dynamic of evaporation process.

scholarly journals A New Method for Evaporation Modeling: Dynamic Evolving Neural-Fuzzy Inference System

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ozgur Kisi ◽  
Iman Mansouri ◽  
Jong Wan Hu
Keyword(s):  
Fuzzy Inference System ◽  
Cross Validation ◽  
Fuzzy Inference ◽  
New Method ◽  
Mean Square ◽  
Pan Evaporation ◽  
Anfis Model ◽  
Inference System ◽  
Absolute Relative Error ◽  
Neural Fuzzy

Evaporation estimation is very essential for planning and development of water resources. The study investigates the ability of new method, dynamic evolving neural-fuzzy inference system (DENFIS), in modeling monthly pan evaporation. Monthly maximum and minimum temperatures, solar radiation, wind speed, and relative humidity data obtained from two stations located in Turkey are used as inputs to the models. The results of DENFIS method were compared with the classical adaptive neural-fuzzy inference system (ANFIS) by using root mean square error (RMSE), mean absolute relative error (MARE), and Nash-Sutcliffe Coefficient (NS) statistics. Cross validation was applied for better comparison of the models. The results indicated that DENFIS models increased the accuracy of ANFIS models to some extent. RMSE, MARE, and NS of the ANFIS model were increased by 11.13, 11.45, and 6.83% for the Antalya station and 20.11, 12.94%, and 8.29% for the Antakya station using DENFIS.

scholarly journals Comparison of multi-gene genetic programming and dynamic evolving neural-fuzzy inference system in modeling pan evaporation

2017 ◽  
Vol 49 (4) ◽  
pp. 1221-1233 ◽  
Author(s):  
Okan Eray ◽  
Cihan Mert ◽  
Ozgur Kisi
Keyword(s):  
Genetic Programming ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Empirical Method ◽  
Data Driven ◽  
Maximum Temperature ◽  
Climatic Data ◽  
Pan Evaporation ◽  
Inference System ◽  
Neural Fuzzy

AbstractAccurately modeling pan evaporation is important in water resources planning and management and also in environmental engineering. This study compares the accuracy of two new data-driven methods, multi-gene genetic programming (MGGP) approach and dynamic evolving neural-fuzzy inference system (DENFIS), in modeling monthly pan evaporation. The climatic data, namely, minimum temperature, maximum temperature, solar radiation, relative humidity, wind speed, and pan evaporation, obtained from Antakya and Antalya stations, Mediterranean Region of Turkey were utilized in the study. The MGGP and DENFIS methods were also compared with genetic programming (GP) and calibrated version of Hargreaves Samani (CHS) empirical method. For Antakya station, GP had slightly better accuracy than the MGGP and DENFIS models and all the data-driven models performed were superior to the CHS while the DENFIS provided better performance than the other models in modeling pan evaporation at Antalya station. The effect of periodicity input to the models' accuracy was also investigated and it was found that adding periodicity significantly increased the accuracy of MGGP and DENFIS models.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

Sign in / Sign up

Export Citation Format

Share Document