scholarly journals Comparison of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gaussian Process for Machine Learning (GPML) Algorithms for the Prediction of Norovirus Concentration in Drinking Water Supply

2017 ◽  
pp. 74-95 ◽  
Author(s):  
Hadi Mohammed ◽  
Ibrahim A. Hameed ◽  
Razak Seidu
Keyword(s):  
Machine Learning ◽  
Drinking Water ◽  
Water Supply ◽  
Gaussian Process ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Drinking Water Supply ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals Prediction of vitrinite reflectance values using machine learning techniques: a new approach

2020 ◽  
Author(s):  
Zahra Sadeghtabaghi ◽  
Mohsen Talebkeikhah ◽  
Ahmad Reza Rabbani
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Low Cost ◽  
Vitrinite Reflectance ◽  
Empirical Equations ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Rock Eval

AbstractVitrinite reflectance (VR) is considered the most used maturity indicator of source rocks. Although vitrinite reflectance is an acceptable parameter for maturity and is widely used, it is sometimes difficult to measure. Furthermore, Rock-Eval pyrolysis is a current technique for geochemical investigations and evaluating source rock by their quality and quantity of organic matter, which provide low cost, quick, and valid information. Predicting vitrinite reflectance by using a quick and straightforward method like Rock-Eval pyrolysis results in determining accurate and reliable values of VR with consuming low cost and time. Previous studies used empirical equations for vitrinite reflectance prediction by the Tmax data, which was accompanied by poor results. Therefore, finding a way for precise vitrinite reflectance prediction by Rock-Eval data seems useful. For this aim, vitrinite reflectance values are predicted by 15 distinct machine learning models of the decision tree, random forest, support vector machine, group method of data handling, radial basis function, multilayer perceptron, adaptive neuro-fuzzy inference system, and multilayer perceptron and adaptive neuro-fuzzy inference system, which are coupled with evolutionary optimization methods such as grasshopper optimization algorithm, bat algorithm, particle swarm optimization, and genetic algorithm, with four inputs of Rock-Eval pyrolysis parameters of Tmax, S1/TOC, HI, and depth for the first time. Statistical evaluations indicate that the decision tree is the most precise model for VR prediction, which can estimate vitrinite reflectance precisely. The comparison between the decision tree and previous proposed empirical equations indicates that the machine learning method performs much more accurately.

scholarly journals Water Supply System Demand Forecasting Using Adaptive Neuro-fuzzy Inference System

2015 ◽  
Vol 4 ◽  
pp. 950-956 ◽  
Author(s):  
D.P. Vijayalaksmi ◽  
K.S. Jinesh Babu
Keyword(s):  
Water Supply ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Demand Forecasting ◽  
Supply System ◽  
Water Supply System ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro-Fuzzy Inference System Model

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Mariamme Mohammed ◽  
Ahmad Sharafati ◽  
Nadhir Al-Ansari ◽  
Zaher Mundher Yaseen
Keyword(s):  
Machine Learning ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Pso Algorithm ◽  
Shallow Foundation ◽  
Inference System ◽  
Predictive Variables ◽  
Neuro Fuzzy ◽  
Initial Dataset ◽  
Statistical Metrics

Settlement simulating in cohesion materials is a crucial issue due to complexity of cohesion soil texture. This research emphasis on the implementation of newly developed machine learning models called hybridized Adaptive Neuro-Fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) algorithm, Ant Colony optimizer (ACO), Differential Evolution (DE), and Genetic Algorithm (GA) as efficient approaches to predict settlement of shallow foundation over cohesion soil properties. The width of footing (B), pressure of footing (qa), geometry of footing (L/B), count of SPT blow (N), and ratio of footing embedment (Df/B) are considered as predictive variables. Nonhomogeneity and inconsistency of employed dataset is a major concern during prediction modeling. Hence, two different modeling scenarios (i) preprocessed dataset (PP) and (ii) nonprocessed (initial) dataset (NP) were inspected. To assess the accuracy of the applied hybrid models and standalone one, multiple statistical metrics were computed and analyzed over the training and testing phases. Results indicated ANFIS-PSO model exhibited an accurate and reliable prediction data intelligent and had the highest predictability performance against all employed models. In addition, results demonstrated that data preprocessing is highly essential to be performed prior to building the predictive models. Overall, ANFIS-PSO model showed a robust machine learning for settlement prediction.

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

scholarly journals Runoff estimation using modified adaptive neuro-fuzzy inference system

2019 ◽  
Vol 25 (4) ◽  
pp. 545-553 ◽  
Author(s):  
Amitabha Nath ◽  
Fisokuhle Mthethwa ◽  
Goutam Saha
Keyword(s):  
Machine Learning ◽  
Fuzzy Inference System ◽  
Water Resource Management ◽  
Flood Control ◽  
Fuzzy Inference ◽  
Arima Model ◽  
Machine Learning Algorithms ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Runoff Modeling

Rainfall-Runoff modeling plays a crucial role in various aspects of water resource management. It helps significantly in resolving the issues related to flood control, protection of agricultural lands, etc. Various Machine learning and statistical-based algorithms have been used for this purpose. These techniques resulted in outcomes with an acceptable rate of success. One of the pertinent machine learning algorithms namely Adaptive Neuro Fuzzy Inference System (ANFIS) has been reported to be a very effective tool for the purpose. However, the computational complexity of ANFIS is a major hindrance in its application. In this paper, we resolved this problem of ANFIS by incorporating one of the evolutionary algorithms known as Particle Swarm Optimization (PSO) which was used in estimating the parameters pertaining to ANFIS. The results of the modified ANFIS were found to be satisfactory. The performance of this modified ANFIS is then compared with conventional ANFIS and another popular statistical modeling technique namely ARIMA model with respect to the forecasting of runoff. In the present investigation, it was found that proposed PSO-ANFIS performed better than ARIMA and conventional ANFIS with respect to the prediction accuracy of runoff.

scholarly journals Comparative Analysis of Single and Hybrid Neuro-Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System

2020 ◽  
Author(s):  
Sina Ardabili ◽  
Bertalan Beszedes ◽  
Laszlo Nadai ◽  
Karoly Szell ◽  
Amir Mosavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fuzzy Inference System ◽  
Air Conditioning ◽  
High Performance ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Save Energy

The hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models. Hybrid machine learning models, particularly, have gained popularity in the advancement of the high-performance control systems. Higher accuracy and better performance for prediction models of exergy destruction and energy consumption used in the control circuit of heating, ventilation, and air conditioning (HVAC) systems can be highly economical in the industrial scale to save energy. This research proposes two hybrid models of adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), and adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) for HVAC. The results are further compared with the single ANFIS model. The ANFIS-PSO model with the RMSE of 0.0065, MAE of 0.0028, and R2 equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models.

scholarly journals Pengendalian Lampu Lalu Lintas Cerdas di Persimpangan Empat Ruas yang Kompleks Menggunakan Algoritma Adaptive Neuro Fuzzy Inference System

2020 ◽  
Vol 6 (1) ◽  
pp. 29
Author(s):  
Budy Santoso ◽  
Azminuddin I. S. Azis ◽  
Andi Bode
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Waiting Times ◽  
Inference System ◽  
Arrival Times ◽  
Neuro Fuzzy ◽  
Transportation Type

Masalah transportasi masih sering dihadapkan pada fenomena kemacetan arus lalu lintas yang berdampak pada kecelakaan lalu lintas, polusi, dan kerugian ekonomi. Salah satu cara untuk meminimalisir fenomena tersebut melalui pengendalian sistem lampu lalu lintas yang baik terhadap arus lalu lintas jangka pendek di persimpangan jalan. Pengendalian lampu lalu lintas secara statis terbukti belum optimal dalam meminimalisir kemacetan arus lalu lintas, salah satu penyebabnya karena kondisi arus lalu lintas yang bervariasi sehingga tidak mudah diprediksi. Fuzzy Inference System (FIS) sering terbukti mampu menunjukkan hasil yang lebih baik daripada pengendalian lampu lalu lintas secara statis. Namun FIS tidak dapat diterapkan pada kondisi arus lalu lintas yang bervariasi atau di persimpangan jalan yang berbeda karena metode tersebut tidak mampu mempelajari kondisi arus lalu lintas secara real time. Agar FIS mampu melakukan pembelajaran, maka pendekatan machine learning dapat diterapkan pada FIS. Salah satu pengembangannya adalah Adaptive Neuro Fuzzy Inference System (ANFIS) yang dapat mengendalikan lampu lalu lintas cerdas secara dinamis dengan hasil yang lebih baik daripada FIS. Namun umumnya ANFIS diuji coba pada persimpangan jalan yang normal. Bagaimana jika di persimpangan yang kompleks? Persimpangan yang memiliki beberapa ruas/jalur utama yang besar (jalur poros), sementara ruas laiinya kecil, bahkan terdapat ruas yang tidak berpotongan, sehingga ada prioritas untuk setiap ruasnya. Hasilnya, penerapan ANFIS (3 GaussMf) untuk pengendalian lampu lalu lintas cerdas/dinamis di persimpangan empat ruas yang kompleks mampu mereduksi Average Waiting Times (AWT) rata-rata sebesar 3,4071E-05 detik dengan 2,7156 RMSE rata-rata, menggunakan variabel Queues Quantity dan Priority Degree. Sedangkan jika menggunakan variabel Arrival Times, Transportation Type, dan Goal Junction, ANFIS (4 TrapMf) mampu mereduksi AWT sebesar 0,0779 detik dengan 19,7646 RMSE.

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.

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