scholarly journals Comparative Study of Daily Rainfall Forecasting Models Using Adaptive-Neuro Fuzzy Inference System (ANFIS)

2015 ◽  
Vol 10 (2) ◽  
pp. 529-536 ◽  
Author(s):  
M. A Sojitra ◽  
P. A Pandya
Keyword(s):  
Relative Humidity ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Moving Average ◽  
Daily Rainfall ◽  
Rainfall Forecasting ◽  
Inference System ◽  
Forecasting Models ◽  
Mean Temperature ◽  
Neuro Fuzzy

The study was carried out to develop rainfall forecasting Models. Adaptive Neuro-Fuzzy Inference System (ANFIS) was used for developing Models rainfall of Udaipur city. Two data sets were prepared using 35 year of weather parameters i.e. wet bulb temperature, mean temperature, relative humidity and evaporation of previous day and previous moving average week were used to prepare case I and case II respectively. Gaussian and Generalized Bell membership functions were used to prepare models. Statistical and hydrologic performance indices of ANFIS (Gaussian, 5) gave better performance among developed four models. The study showed that sensitivity analysis revealed wet bulb temperature is most sensible parameter followed by mean temperature, relative humidity and evaporation.

scholarly journals Rainfall Forecasting in Banyuwangi Using Adaptive Neuro Fuzzy Inference System

2017 ◽  
Vol 1 (2) ◽  
pp. 65 ◽  
Author(s):  
Gusti Ahmad Fanshuri Alfarisy ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Hybrid Learning ◽  
Learning Method ◽  
Rainfall Forecasting ◽  
Inference System ◽  
Previous Event ◽  
Neuro Fuzzy ◽  
High Prediction

Rainfall forcasting is a non-linear forecasting process that varies according to area and strongly influenced by climate change. It is a difficult process due to complexity of rainfall trend in the previous event and the popularity of Adaptive Neuro Fuzzy Inference System (ANFIS) with hybrid learning method give high prediction for rainfall as a forecasting model. Thus, in this study we investigate the efficient membership function of ANFIS for predicting rainfall in Banyuwangi, Indonesia. The number of different membership functions that use hybrid learning method is compared. The validation process shows that 3 or 4 membership function gives minimum RMSE results that use temperature, wind speed and relative humidity as parameters.

Application of adaptive neuro-fuzzy inference system for strength prediction of rubberized concrete containing silica fume and zeolite

2019 ◽  
Vol 234 (3) ◽  
pp. 438-451 ◽  
Author(s):  
Mostafa Jalal ◽  
Poura Arabali ◽  
Zachary Grasley ◽  
Jeffrey W Bullard
Keyword(s):  
Compressive Strength ◽  
Relative Humidity ◽  
Silica Fume ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Performance Criteria ◽  
System Model ◽  
Rubberized Concrete ◽  
Inference System ◽  
Neuro Fuzzy

Rubberized concrete containing waste tire rubber, silica fume, and zeolite cured in different curing conditions has been investigated in this paper. For this purpose, coarse aggregates were partially replaced by different percentages of waste rubber chips, namely 10% and 15%, and silica fume and zeolite were incorporated into the binder to replace 10% of cement mass. Different mixes were made and cured in two different conditions, namely in water and air with relative humidity of 100% and 50%, respectively. Compressive strengths of mixes were measured at different ages as 3, 7, 28, and 42 days. In order to simulate and predict the compressive strength of the rubberized cement composite, the influencing parameters were considered as cement content, silica fume, zeolite, rubber percentage, relative humidity, and age of the samples. Then, adaptive neuro-fuzzy inference system was employed to develop a prediction model for compressive strength of the concrete. Six variables were introduced into the adaptive neuro-fuzzy inference system model as inputs and the compressive strength was considered as the output. Prediction results and performance criteria were determined for various datasets including training, validation, testing, and all data. Parametric study of the adaptive neuro-fuzzy inference system models was also conducted to investigate the effect of each variable on the compressive strength of the rubberized concrete. Based on the correlations and errors obtained from the model, it was found that the proposed adaptive neuro-fuzzy inference system model can be a robust tool for predicting the behavior of complex composite materials such as rubberized concrete.

scholarly journals Monthly Average Rainfall Forecasting Based On An Adaptive Neuro-Fuzzy Inference System in Upper Brahmani Basin, Odisha, India

2022 ◽  
Author(s):  
M.Uma Maheswar Rao ◽  
Kanhu Charan Patra ◽  
Suvendu Kumar Sasmal
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Membership Functions ◽  
Climate Data ◽  
Rainfall Forecasting ◽  
Anfis Model ◽  
Inference System ◽  
Neural Network Learning ◽  
Neuro Fuzzy ◽  
Learning Capabilities

Abstract Floods disrupt human activities, resulting in the loss of lives and property of a region. Excessive rainfall is one of the reasons for flooding, especially in the downstream areas of a catchment. Because of its complexity, understanding and forecasting rainfall is incredibly a challenge. This study investigates the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) in predicting rainfall using several surface weather parameters as predictors. An ANFIS model is developed for forecasting rainfall over the Upper Brahmani Basin by using 30 years of climate data. A hybrid model with six membership functions gives the best forecast for an area. The suggested method blends neural network learning capabilities with language representations of fuzzy systems that are transparent. The application of ANFIS is to the upper Brahmani river basin is tried for the first time. The ANFIS model with various input structures and membership functions has been built, trained, and tested to evaluate the capability of the model. Statistical performance indices are used to evaluate the performance. Using the developed model, forecast is done for year 2021 – 2030.

scholarly journals ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR OKRA YIELD PREDICTION

2017 ◽  
Vol 15 (2) ◽  
pp. 95-102
Author(s):  
O A OJESANMI ◽  
A D ADEKOYA ◽  
A A AWOSEYI
Keyword(s):  
Relative Humidity ◽  
Fuzzy Inference System ◽  
Minimum Temperature ◽  
Fuzzy Inference ◽  
Meteorological Data ◽  
Maximum Temperature ◽  
Yield Prediction ◽  
Yield Data ◽  
Inference System ◽  
Neuro Fuzzy

This paper, adaptive neuro-fuzzy inference system for okra yield prediction, describes the use of neuro-fuzzy inference system in the prediction of okra yield using environmental parameters such as minimum temperature, relative humidity, evaporation, sunshine hours, rainfall and maximum temperature as input into the neuro-fuzzy inference system, and yield as output. The agro meteorological data used were obtained from the department of agro meteorological and water management, Federal University of Agriculture, Abeokuta and the yield data were obtained from the Department of Horticulture, Federal University of Agriculture, Abeokuta. MATLAB was used for the analysis of the data. From the results, the maximum predicted yield showed that at minimum temperature of 24.4 oc, relative humidity of 78.3% and evaporation of 5.5mm, the yield predicted is 1.67 tonnes/hectare. 

scholarly journals Electricity consumption forecast for Tarkwa using autoregressive integrated moving average and adaptive neuro fuzzy inference system

2021 ◽  
Vol 18 (1) ◽  
pp. 75-94
Author(s):  
Robert Ofosu ◽  
Benjamin Odoi ◽  
Mercy Asamoah
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Moving Average ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Inference System ◽  
Autoregressive Integrated Moving Average ◽  
Independent Variables ◽  
Testing Dataset ◽  
Neuro Fuzzy

Electricity has become one of the inelastic goods in our world today. The proper functioning of most equipment today relies on electricity. Taking Tarkwa which is a mining community into consideration, the various mines, schools, shops, banks and other companies in the municipality massively rely on electricity for their day to day running. Therefore, knowing the exact amount of electricity to produce and distribute for the smooth running of businesses and basic living is of great necessity. This study compared and formulated a model to forecast and predict the daily electrical energy consumption in Tarkwa for the year 2019. The data used was a monthly dataset for the year 2018 and it comprised of the temperature, wind speed, population and electricity consumption for Tarkwa. The methods used were Artificial Neuro-Fuzzy Inference System (ANFIS) and Autoregressive Integrated Moving Average (ARIMA). The ANFIS was used as a predictor to predict the electricity consumption based on the training and testing of the dependent and independent variables. The ARIMA was used to forecast the dependent and independent variables for 2019. These simulations were done using MATLand Minitab. The results of the analysis revealed that the training and testing dataset allowed ANFIS to learn and understand the system but the ANFIS could only forecast the 2019 electricity consumption after the input data to the system was changed to the ARIMA forecasted 2019 independent variables. It was observed that the amount of electricity consumed in 2019 increased by 14%.

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