scholarly journals Neuro-fuzzy inference system Prediction of stability indices and Sodium absorption ratio in Lordegan rural drinking water resources in west Iran

Data in Brief ◽  
2018 ◽  
Vol 18 ◽  
pp. 255-261 ◽  
Author(s):  
Afshin Takdastan ◽  
Majid Mirzabeygi (Radfard) ◽  
Mahmood Yousefi ◽  
Abbas Abbasnia ◽  
Rouhollah Khodadadia ◽  
...  
Keyword(s):  
Drinking Water ◽  
Water Resources ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Sodium Absorption ◽  
Inference System ◽  
Sodium Absorption Ratio ◽  
Drinking Water Resources ◽  
Neuro Fuzzy ◽  
Stability Indices

Adaptive Neuro-fuzzy Inference System (ANFIS) Modelling in Energy System and Water Resources

2019 ◽  
pp. 117-133 ◽  
Author(s):  
P. A. Adedeji ◽  
S. O. Masebinu ◽  
S. A. Akinlabi ◽  
N. Madushele
Keyword(s):  
Water Resources ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Energy System ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals Prediction of daily streamflow using artificial neural networks (ANNs), wavelet neural networks (WNNs), and adaptive neuro-fuzzy inference system (ANFIS) models

2020 ◽  
Vol 20 (4) ◽  
pp. 1396-1408
Author(s):  
Hüseyin Yıldırım Dalkiliç ◽  
Said Ali Hashimi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Water Resources ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Hydrological Processes ◽  
Inference System ◽  
Daily Streamflow ◽  
Neuro Fuzzy ◽  
Artificial Neural

Abstract In recent years, the prediction of hydrological processes for the sustainable use of water resources has been a focus of research by scientists in the field of hydrology and water resources. Therefore, in this study, the prediction of daily streamflow using the artificial neural network (ANN), wavelet neural network (WNN) and adaptive neuro-fuzzy inference system (ANFIS) models were taken into account to develop the efficiency and accuracy of the models' performances, compare their results and explain their outcomes for future study or use in hydrological processes. To validate the performance of the models, 70% (1996–2007) of the data were used to train them and 30% (2008–2011) of the data were used to test them. The estimated results of the models were evaluated by the root mean square error (RMSE), determination coefficient (R2), Nash–Sutcliffe (NS), and RMSE-observation standard deviation ratio (RSR) evaluation indexes. Although the outcomes of the models were comparable, the WNN model with RMSE = 0.700, R2 = 0.971, NS = 0.927, and RSR = 0.270 demonstrated the best performance compared to the ANN and ANFIS models.

scholarly journals Modelling nitrate concentration of groundwater using adaptive neural-based fuzzy inference system

2012 ◽  
Vol 7 (No. 2) ◽  
pp. 73-83 ◽  
Author(s):  
S.F. Mousavi ◽  
M.J. Amiri
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Nitrate Concentration ◽  
Sodium Absorption ◽  
Inference System ◽  
Sodium Absorption Ratio ◽  
Isfahan Province ◽  
High Nitrate Concentration ◽  
Agricultural Areas ◽  
Observation Wells

High nitrate concentration in groundwater is a major problem in agricultural areas in Iran. Nitrate pollution in groundwater of the particular regions in Isfahan province of Iran has been investigated. The objective of this study was to evaluate the performance of Adaptive Neural-Based Fuzzy Inference System (ANFIS) for estimating the nitrate concentration. In this research, 175 observation wells were selected and nitrate, potassium, magnesium, sodium, chloride, bicarbonate, sulphate, calcium and hardness were determined in groundwater samples for five consecutive months. Electrical conductivity (EC) and pH were also measured and the sodium absorption ratio (SAR) was calculated. The five-month average of bicarbonate, hardness, EC, calcium and magnesium are taken as the input data and the nitrate concentration as the output data. Based on the obtained structures, four ANFIS models were tested against the measured nitrate concentrations to assess the accuracy of each model. The results showed that ANFIS1 was the most accurate (RMSE = 1.17 and R<sup>2</sup> = 0.93) and ANFIS4 was the worst (RMSE = 2.94 and R<sup>2</sup> = 0.68) for estimating the nitrate concentration. In ranking the models, ANFIS2 and ANFIS3 ranked the second and third, respectively. The results showed that all ANFIS models underestimated the nitrate concentration. In general, the ANFIS1 model is recommendable for prediction of nitrate level in groundwater of the studied region.

scholarly journals Rainfall Prediction Using Adaptive Neuro-Fuzzy Inference System

2018 ◽  
pp. 23-25
Author(s):  
Ozlem Terzi ◽  
Onur Ozcanoglu ◽  
Tahsin Baykal
Keyword(s):  
Water Resources ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Rainfall Prediction ◽  
Neuro Fuzzy

The rainfall prediction is of great importance in the utilization and planning of water resources. In this study, the validity of Adaptive Neuro-Fuzzy Inference System (ANFIS) in rainfall prediction is investigated. The ANFIS models are developed with different input combinations and it is observed that ANFIS models give successful results in rainfall prediction.

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.

Prediction of the Number of Airport Passengers Using Fuzzy C-Means and Adaptive Neuro Fuzzy Inference System

2017 ◽  
Vol 10 (3) ◽  
pp. 280
Author(s):  
Sunarsan Sitohang ◽  
Abba Suganda Girsang ◽  
Suharjito Suharjito
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Fuzzy C Means ◽  
Neuro Fuzzy

PENGEMBANGAN SISTEM MANAJEMEN PENDUKUNG KEPUTUSAN PENILAIAN MUTU KEPEGAWAIAN DINAS PENDIDIKAN REMBANG MENGGUNAKAN ALGORITMA ANFIS (ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM)

2020 ◽  
Vol 12 (1) ◽  
pp. 6-17
Author(s):  
Angga debby frayudha ◽  
Aris Yulianto ◽  
Fatmawatul Qomariyah
Keyword(s):  
Fuzzy Inference System ◽  
Industry 4.0 ◽  
Fuzzy Inference ◽  
System Model ◽  
Inference System ◽  
Neuro Fuzzy

Di era revolusi industry 4.0 terdapat banyak sekali kemudahan yang diberikan teknologi kepada manusia. Tentu ini akan menjadi baik apabila manusia mampu memanfaatkan hal tersebut dengan baik pula. Namun disisi lain juga bisa mengakibatkan dampak negative terhadap manusia, misalnya dengan adanya internet bisa mengakibatkan manusia melakukan penipuan di media social. Selain itu dengan canggihnya teknologi dapat menjadikan manusia menjadi malas yang bisa berimbas menurunnya kualitas sumber daya manusia. Maka dari itu untuk menghadapi hal ini perlu menyiapkan pendidikan yang baik.Pendidikan akan berjalan baik apabila lembaga yang mengurusnya berkompeten dalam melakukan tugasnya .Penulis coba memberikan ide untuk memprediksi kinerja pegawai Dinas Pendidikan Kabupaten Rembang menggunakan mentode ANFIS (Adaptive Neuro Fuzzy Inference System) guna untuk membantu lembaga tersebut menyeleksi maupun menilai kinerja karyawan demi meningkatkan kualitas dari segi sumber daya manusia. ANFIS merupakan jaringan adaptif yang berbasis pada sistem kesimpulan fuzzy (fuzzy inference system). Model penilaian kinerja pegawai di Dinas Pendidikan Kabupaten Rembang dengan menggunakan Adaptive Neuro-Fuzzy Inference System (ANFIS) menghasilkan penilaian  yang lebih baik dan akurat.  Hasil pengujian metode tersebut memiliki nilai akurasi 65%. Dengan metode ANFIS (Adaptive Neuro Fuzzy Inference System) dapat memprediksi kinerja karyawan sebagai salah satu pengambilan keputusan terhadap kinerja pegawai. Selain itu nantinya system penlaian kinerja pegawai akan lebih tertata dan efisien.

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