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Daily pan evaporation estimation using gene expression programming and adaptive neural-based fuzzy inference system

neural COMPUTING & APPLICATIONS 2013年第3-4期23卷 1035-1044页

作者： Terzi, Ozlem Suleyman Demirel Univ TR-32260 Isparta Turkey

This study was conducted using gene expression programming (GEP) and an adaptive neural-based fuzzy inference system (ANFIS) as an alternative approach to estimate daily pan evaporation, which is an important parameter in hydrological and meteorological studies. The input parameters used to estimate daily pan evaporation from Lake Egirdir in the southwestern part of Turkey are the daily pan evaporation data of Lake Kovada (Ko (t) ) and Lake Karacaoren Dam (Ka (t) ) and the previous 1-, 2-, and 3-day pan evaporation values of Lake Egirdir. The various input combinations were tried by using pan evaporation data for the years 1998-2005. The GEP model with the highest Nash-Sutcliffe efficiency and the lowest mean square error have the daily pan evaporation data of Lake Kovada (Ko (t) ) and Lake Karacaoren Dam (Ka (t) ) and the previous 1-day pan evaporation values of Lake Egirdir. The NSE of the best GEP model was obtained as 0.729, 0.722, and 0.701 for training, testing, and validation sets, respectively. Furthermore, the ANFIS models were developed using the same input combinations. It was seen that the GEP model was more superior to the ANFIS model.

关键词： Daily pan evaporation Gene expression programming adaptive neural-based fuzzy inference system Lake Egirdir

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adaptive neural fuzzy inference system for feeding decision-making of grass carp (Ctenopharyngodon idellus) in outdoor intensive culturing ponds

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AQUACULTURE 2019年 498卷 28-36页

作者： Zhao, Siqi Ding, Weimin Zhao, Sanqin Gu, Jiabing Nanjing Agr Univ Coll Engn Nanjing 210031 Jiangsu Peoples R China Engn Lab Modern Facil Agr Technol & Equipment Jia Nanjing 210031 Jiangsu Peoples R China

Feed is the most main expenditure in outdoor intensive culture system. The improvement of feeding efficiency has great significance for increasing production and reducing costs. Presently, with the development of precision agriculture, automatic adjustments of the feeding amount according to the demands of the fish has become a developing trend. The objective of this paper was to develop an automatic feeding decision-making system based on the water quality parameters to solve the problem of inefficiency in artificial feeding control. In this study, we proposed an effective control method using adaptive neural fuzzy inference system (ANFIS) to achieve this purpose. First, two input variables (dissolved oxygen saturation [DO];temperature [T]) and one output variable (feeding percent [FP]) were selected and defined. Second, the model of the linguistic variables and the optimal fuzzy rule base were obtained by the training and learning by utilization of hybrid learning methods. Finally, an ANFIS controller for on-demand feeding was developed and the performance was compared with fuzzy logic control (FLC) and artificial control (AC) by the Nash-Sutcliffe efficiency coefficient (NS), the root mean squared error (RMSE), and fish growth parameters. The results indicated that the NS and RMSE of the ANFIS model were 0.8539 and 0.0541, respectively, and were better for forecasting feeding decisions compared with the FLC and AC methods. Compared with the AC, there was no significant differences in promoting fish growth (P > 0.05), whereas the feed conversion rate (FCR) was reduced by14.35%. In addition, the mean of ammonia nitrogen concentration decreased by 22.59%, and the mean of turbidity increased 5.5 cm to 28.9 cm, reducing eutrophication and pollution of water in pond. Therefore, applying those approaches based on ANFIS control to the feeding decision system in outdoor intensive culturing is flexible and effective, and has potential for the design of fine feeding equipme

关键词： Grass carp Outdoor intensive culture system Precision agriculture Feeding decision-making system adaptive neural-based fuzzy inference system

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Evaluating the performances of several artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management

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SUSTAINABLE CITIES AND SOCIETY 2021年 64卷 102562-102562页

作者： Niu, Wen-jing Feng, Zhong-kai ChangJiang Water Resources Commiss Bur Hydrol Wuhan 430010 Peoples R China Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China

Accurate runoff forecasting plays an important role in guaranteeing the sustainable utilization and management of water resources. Artificial intelligence methods can provide new possibilities for runoff prediction when the underlying physical relationship cannot be explicitly obtained. However, few reports evaluate the performances of various artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management by far. To refill this research gap, the potentials of five artificial intelligence methods in daily streamflow series prediction are examined, including artificial neural network (ANN), adaptive neural based fuzzy inference system (ANFIS), extreme learning machine (ELM), Gaussian process regression (GPR) and support vector machine (SVM). Four quantitative statistical indexes are chosen as the evaluation benchmarks. The results from two huge hydropower reservoirs in China show that five artificial intelligence methods can achieve satisfying forecasting results, while the SVM, GPR and ELM methods can produce better performances than ANN and ANFIS in both training and testing phases with respective to four indexes. Thus, it is of great importance to carefully choose the appropriate forecasting models based on the actual characteristics of the studied reservoir.

关键词： Runoff time series prediction Artificial neural network adaptive neural-based fuzzy inference system Extreme learning machine Support vector machine Gaussian process regression

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Estimation of dissolved oxygen using data-driven techniques in the Tai Po River, Hong Kong

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ENVIRONMENTAL EARTH SCIENCES 2015年第5期74卷 4065-4073页

作者： Nemati, Samira Fazelifard, Mohammad Hasan Terzi, Ozlem Ghorbani, Mohammad Ali Univ Tabriz Fac Agr Dept Water Engn Tabriz Iran Suleyman Demirel Univ Tech Educ Fac TR-32260 Isparta Turkey

This study investigates the applicability of multilinear regression (MLR), adaptive neural-based fuzzy inference system (ANFIS) and artificial neural networks (ANN) methods from data-driven techniques in estimation of the dissolved oxygen (DO), which is an important parameter in water quality, aquatic life, efficient water management and health care planning studies. The measured parameters covering 21 years of sample data for the years 1991-2011 in the Tai Po River, Hong Kong, were used to develop the models. The input parameters used to estimate DO are chloride (Cl), pH value (pH), electrical conductivity, temperature (Temp), nitrite nitrogen (NO2-N), nitrate nitrogen (NO3-N), ammonia nitrogen (NH4-N) and total phosphorous (T-P). The performance of the developed models was evaluated through the three performance criteria: correlation coefficient, root mean square error and the Nash-Sutcliffe efficiency coefficient. When the results of the developed models were compared with DO measurements using performance criteria, the ANN model shows better performance than the MLR and ANFIS models in estimation of DO concentration. Also, a sensitivity analysis was carried out to evaluate the relative importance of the input parameters in estimation of the DO. The most effective input parameters are determined as Cl, Temp, NO3-N, NO2-N, NH4-N and T-P parameters, respectively. Furthermore, the pH variable has the least contribution on the ANN model.

关键词： Multilinear regression adaptive neural-based fuzzy inference system Artificial neural networks Dissolved oxygen Tai Po River

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Modeling for pavement roughness using the ANFIS approach

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ADVANCES IN ENGINEERING SOFTWARE 2013年 57卷 59-64页

作者： Terzi, Serdal Suleyman Demirel Univ Fac Engn TR-32260 Isparta Turkey

The term "present serviceability" was adopted to represent the momentary ability of pavement to serve traffic, and the performance of the pavement was represented by its serviceability history in conjunction with its load application history. Serviceability was found to be influenced by longitudinal and transverse profile as well as the extent of cracking and patching. The amount of weight that should be assigned to each element in the determination of overall serviceability is a matter of subjective opinion. In this study, an adaptive neural-based fuzzy inference system (ANFIS) method is used in modeling the International Roughness Index (IRI) of flexible pavements. Data from the LTPP IMS database, namely, age, cumulative Equivalent Single Axle Loads (ESALs), and Structure Number (SN) were used in the modeling. Results showed that the ANFIS model is successful for the estimation of IRI, and this model can be easily applied in different regions. The model can be further developed by combining expert judgment and newly measured data. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Flexible highway pavements International Roughness Index (IRI) adaptive neural-based fuzzy inference system Pavement performance Structure number Equivalent Single Axle Loads

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