scholarly journals Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8882
Author(s):  
Abolghasem Sadeghi-Niaraki ◽  
Ozgur Kisi ◽  
Soo-Mi Choi
Keyword(s):  
Absolute Error ◽  
Geographical Information ◽  
Subtractive Clustering ◽  
Fuzzy Methods ◽  
Air Temperatures ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering ◽  
Fuzzy Genetic ◽  
Gp Model

This paper investigates the capabilities of the evolutionary fuzzy genetic (FG) approach and compares it with three neuro-fuzzy methods—neuro-fuzzy with grid partitioning (ANFIS-GP), neuro-fuzzy with subtractive clustering (ANFIS-SC), and neuro-fuzzy with fuzzy c-means clustering (ANFIS-FCM)—in terms of modeling long-term air temperatures for sustainability based on geographical information. In this regard, to estimate long-term air temperatures for a 40-year (1970–2011) period, the models were developed using data for the month of the year, latitude, longitude, and altitude obtained from 71 stations in Turkey. The models were evaluated with respect to mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2). All data were divided into three parts and every model was tested on each. The FG approach outperformed the other models, enhancing the MAE, RMSE, NSE, and R2 of the ANFIS-GP model, which yielded the highest accuracy among the neuro-fuzzy models by 20%, 30%, and 4%, respectively. A geographical information system was used to obtain temperature maps using estimates of the optimal models, and the results of the model were assessed using it.

scholarly journals Neuro-fuzzy systems to estimate reference evapotranspiration

Water SA ◽  
2019 ◽  
Vol 45 (2 April) ◽  
Author(s):  
Mousaab Zakhrouf ◽  
Hamid Bouchelkia ◽  
Madani Stamboul
Keyword(s):  
Meteorological Data ◽  
Regional Scale ◽  
Subtractive Clustering ◽  
Efficiency Coefficient ◽  
Climate Data ◽  
Neuro Fuzzy ◽  
Physically Based ◽  
Fuzzy C Means Clustering ◽  
Daily Evapotranspiration ◽  
Semi Empirical

Routine and rapid estimation of evapotranspiration (ET) at regional scale is of great significance for agricultural, hydrological and climatic studies. A large number of empirical or semi-empirical equations have been developed for assessing ET from meteorological data. The FAO-56 PM is one of the most important methods used to estimate evapotranspiration. The advantage of FAO-56 PM is a physically based method that requires a large number of climatic parameter data. In this paper, the potential of two types of neuro-fuzzy system, including ANFIS based on subtractive clustering (S_ANFIS), ANFIS based on the fuzzy C-means clustering method (F_ANFIS), and multiple linear regression (MLR), were used in modelling daily evapotranspiration (ET0). For this purpose various daily climate data – air temperature (T), relative humidity (RH), wind speed (U) and insolation duration (ID) – from Dar El Beidain Algiers, Algeria, were used as inputs for the ANFIS and MLR models to estimate the ET0 obtained by FAO-56 based on the Penman-Monteith equation. The obtained results show that the performances of S_ANFIS model yield superior to those of F_ANFIS and MLR models. It can be judged from results of the Nash-Sutcliffe efficiency coefficient (EC) where S_ANFIS (EC = 94.01%) model can improve the performances of F_ANFIS (EC = 93.00%) and MLR (EC = 92.12%) during the test period, respectively.

scholarly journals Predicting H2S emission from gravity sewer using an adaptive neuro-fuzzy inference system

2021 ◽  
Author(s):  
R. Salehi ◽  
S. Chaiprapat
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Predictive Performance ◽  
Subtractive Clustering ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Hydraulic Conditions ◽  
Gravity Sewer ◽  
H2s Emission ◽  
Gp Model

Abstract A predictive model to estimate hydrogen sulfide (H2S) emission from sewers would offer engineers and asset managers the ability to evaluate the possible odor/corrosion problems during the design and operation of sewers to avoid in-sewer complications. This study aimed to model and forecast H2S emission from a gravity sewer, as a function of temperature and hydraulic conditions, without requiring prior knowledge of H2S emission mechanism. Two different adaptive neuro-fuzzy inference system (ANFIS) models using grid partitioning (GP) and subtractive clustering (SC) approaches were developed, validated, and tested. The ANFIS-GP model was constructed with two Gaussian membership functions for each input. For the development of the ANFIS-SC model, the MATLAB default values for clustering parameters were selected. Results clearly indicated that both the best ANFIS-GP and ANFIS-SC models produced smaller error compared with the multiple regression models and demonstrated a superior predictive performance on forecasting H2S emission with an excellent R2 value of >0.99. However, the ANFIS-GP model possessed fewer rules and parameters than the ANFIS-SC model. These findings validate the ANFIS-GP model as a potent tool for predicting H2S emission from gravity sewers.

Modeling velocity distributions in small streams using different neuro-fuzzy and neural computing techniques

2019 ◽  
Vol 11 (2) ◽  
pp. 390-401 ◽  
Author(s):  
Onur Genc ◽  
Ozgur Kisi ◽  
Mehmet Ardiclioglu
Keyword(s):  
Velocity Distribution ◽  
Accurate Estimation ◽  
Subtractive Clustering ◽  
Neuro Fuzzy ◽  
Small Streams ◽  
Comparison Results ◽  
Gp Model ◽  
Mean Square Errors ◽  
Better Than ◽  
Mlr Model

Abstract Accurate estimation of velocity distribution in open channels or streams (especially in turbulent flow conditions) is very important and its measurement is very difficult because of spatio-temporal variation in velocity vectors. In the present study, velocity distribution in streams was estimated by two different artificial neural networks (ANN), ANN with conjugate gradient (ANN-CG) and ANN with Levenberg–Marquardt (ANN-LM), and two different adaptive neuro-fuzzy inference systems (ANFIS), ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC). The performance of the proposed models was compared with the multiple-linear regression (MLR) model. The comparison results revealed that the ANN-CG, ANN-LM, ANFIS-GP, and ANFIS-SC models performed better than the MLR model in estimating velocity distribution. Among the soft computing methods, the ANFIS-GP was observed to be better than the ANN-CG, ANN-LM, and ANFIS-SC models. The root mean square errors (RMSE) and mean absolute errors (MAE) of the MLR model were reduced by 69% and 72%, respectively, using the ANFIS-GP model to estimate velocity distribution in the test period.

On the dynamics of distribution of the American White Butterfly, Hyphantria cunea Drury, 1973 (Lepidoptera, Arctiidae), in Ukraine regarding the annual minimal air temperatures

2018 ◽  
Vol 14 (1) ◽  
pp. 44-57
Author(s):  
S. N. Shumov
Keyword(s):  
Air Temperature ◽  
Annual Reports ◽  
Complete Elimination ◽  
Hyphantria Cunea ◽  
White American ◽  
Gis Technologies ◽  
Data Analyses ◽  
Air Temperatures ◽  
Negative Temperatures

The spatial analysis of distribution and quantity of Hyphantria cunea Drury, 1973 across Ukraine since 1952 till 2016 regarding the values of annual absolute temperatures of ground air is performed using the Gis-technologies. The long-term pest dissemination data (Annual reports…, 1951–1985; Surveys of the distribution of quarantine pests ..., 1986–2017) and meteorological information (Meteorological Yearbooks of air temperature the surface layer of the atmosphere in Ukraine for the period 1951-2016; Branch State of the Hydrometeorological Service at the Central Geophysical Observatory of the Ministry for Emergencies) were used in the present research. The values of boundary negative temperatures of winter diapause of Hyphantria cunea, that unable the development of species’ subsequent generation, are received. Data analyses suggests almost complete elimination of winter diapausing individuals of White American Butterfly (especially pupae) under the air temperature of −32°С. Because of arising questions on the time of action of absolute minimal air temperatures, it is necessary to ascertain the boundary negative temperatures of winter diapause for White American Butterfly. It is also necessary to perform the more detailed research of a corresponding biological material with application to the freezing technics, giving temperature up to −50°С, with the subsequent analysis of the received results by the punched-analysis.

Analysis of Long-term Changes of Days with 25℃ or Higher Air Temperatures in Jeju

2016 ◽  
Vol 7 (1) ◽  
pp. 31 ◽  
Author(s):  
Jae-Won Choi ◽  
Yumi Cha ◽  
Jeoung-Yun Kim ◽  
Cheol-Hong Park
Keyword(s):  
Air Temperatures ◽  
Long Term Changes
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