Invasive weed optimization-based adaptive neuro-fuzzy inference system hybrid model for sediment transport with a bed deposit

2020 ◽  
Vol 276 ◽  
pp. 124267 ◽  
Author(s):  
Mir Jafar Sadegh Safari ◽  
Babak Mohammadi ◽  
Katayoun Kargar
Keyword(s):  
Sediment Transport ◽  
Hybrid Model ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals Efficiency of coupled invasive weed optimization-adaptive neuro fuzzy inference system method to assess physical habitats in streams

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Mahdi Sedighkia ◽  
Asghar Abdoli
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Physical Habitat ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Inference System ◽  
Neuro Fuzzy ◽  
System Method ◽  
Habitat Simulation ◽  
Fish Habitats

AbstractThis study presents a coupled invasive weed optimization-adaptive neuro fuzzy inference system method to simulate physical habitat in streams. We implement proposed method in Lar national park in Iran as one of the habitats of Brown trout in southern Caspian Sea basin. Five indices consisting of root mean square error (RMSE), mean absolute error (MAE), reliability index, vulnerability index and Nash–Sutcliffe model efficiency coefficient (NSE) are utilized to compare observed fish habitats and simulated fish habitats. Based on results, measurement indices demonstrate model is robust to assess physical habitats in rivers. RMSE and MAE are 0.09 and 0.08 respectively. Besides, NSE is 0.78 that indicates robustness of model. Moreover, it is necessary to apply developed habitat model in a practical habitat simulation. We utilize two-dimensional hydraulic model in steady state to simulate depth and velocity distribution. Based on qualitative comparison between results of model and observation, coupled invasive weed optimization-adaptive neuro fuzzy inference system method is robust and reliable to simulate physical habitats. We recommend utilizing proposed model for physical habitat simulation in streams for future studies.

An Adaptive Neuro-Based Fuzzy Inference System (ANFIS) for the Prediction of Option Price

2020 ◽  
Vol 11 (2) ◽  
pp. 99-117 ◽  
Author(s):  
Hooman Abdollahi
Keyword(s):  
Hybrid Model ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Option Price ◽  
Prediction Errors ◽  
Parametric Methods ◽  
Option Prices ◽  
Inference System ◽  
Binomial Tree ◽  
Neuro Fuzzy

Option price prediction has been an important issue in the finance literature within recent years. Affected by numerous factors, option price forecasting remains a challenging problem. In this study, a novel hybrid model for forecasting option price consisting of parametric and non-parametric methods is presented. This method is composed of three stages. First, the conventional option pricing methods such as Binomial Tree, Monte Carlo, and Finite Difference are used to primarily calculate the option prices. Next, the author employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) in which the parameters are trained with particle swarm optimization to minimize the prediction errors associated with parametric methods. To select the best input data for the ANFIS structure, which has high mutual information associated with the future option price, the proposed method uses an entropy approach. Experimental examples with data from the Australian options market demonstrate the effectivity of the proposed hybrid model in enhancing the prediction accuracy compared to another method.

scholarly journals A comprehensive study of new hybrid models for Adaptive Neuro-Fuzzy Inference System (ANFIS) with Invasive Weed Optimization (IWO), Differential Evolution (DE), Firefly (FA), Particle Swarm Optimization (PSO) and Bees (BA) algorithms for spatial prediction of groundwater spring potential mapping

2018 ◽  
Author(s):  
Khabat Khosravi ◽  
Mahdi Panahi ◽  
Dieu Tien Bui
Keyword(s):  
Sustainable Management ◽  
Fuzzy Inference ◽  
Spatial Prediction ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Swarm Optimization ◽  
Inference System ◽  
Conditioning Factors ◽  
Neuro Fuzzy ◽  
Potential Mapping

Abstract. Groundwater are one of the most valuable natural resources in the world and their sustainable management is necessary. One of the most important methods in managing groundwater is developing groundwater potential mapping (GPM). The current study benefits from a new hybrids of Adaptive Neuro-Fuzzy Inference System (ANFIS) with five meta-heuristic algorithms, namely Invasive Weed Optimization (IWO), Differential Evolution (DE), Firefly (FA), Particle Swarm Optimization (PSO) and Bees (BA) algorithms for spatial prediction of groundwater spring potential mapping at Koohdasht-Nourabad plain, Lorestan province, Iran. A total number of 2463 springs were identified and then divided in two classes randomly, including 70 % (1725 locations) of the springs were applied for model training and the remaining 30 % (738 spring locations), which were excluded in the training phase, were utilized for the model valuation. Thirteen groundwater occurrence conditioning factors, namely slope degree, slope aspect, altitude, curvature, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distance from fault, distance from river, land-use, rainfall, soil order and lithology (units) have been selected for modeling. The stepwise assessment ratio analysis (SWARA) method was applied to determine the spatial correlation between springs and conditioning factors. The accuracy of the map achieved after applying these five hybrid models was determined using the area under the receiver operating characteristic (ROC) curve (AUC). The results showed that ANFIS-DE has the highest prediction capability (0.875) for groundwater spring potential mapping in the study area, followed by ANFIS-IWO and ANFIS-FA (0.873), ANFIS-PSO (0.865) and ANFIS-BA (0.839). Results of Freidman and Wilcoxon signed rank test revealed that there were statistically significant differences between the models' performances except for ANFIS-FA vs. ANFIS-DE and ANFIS-PSO vs. ANFIS-DE. The results of this research can be useful for decision makers to sustainable management of groundwater resources.

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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