Modelling the daily reference evapotranspiration in semi-arid region of South India: A case study comparing ANFIS and empirical models

Cerro, Ramiro T. Gonzalez del; Subathra, M. S. P.; Kumar, Nallapaneni Manoj; Verrastro, Sebastian; George, S. Thomas

doi:10.1016/j.inpa.2020.02.003

Cited by 35 publications

(14 citation statements)

References 25 publications

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“…This means that contributory influence of nominal variable of soaking strategy, and numeral variables of soaking time and temperature on amount of moisture content was properly prognosticated by the prominent environment developed in the present study. Previous studies(Cerro, Subathra, Kumar, Verrastro, & George, 2021;Elijah, Philomena, Joseph, Charles, & Paschal, 2020;Kaveh et al, 2018;Khoshnevisan, Rafiee, Omid, & Mousazadeh, 2014) also corroborated promising ability of neuro-fuzzy simulation environment for prognostication of desired variables in Biosystems Engineering researches.…”

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confidence: 54%

Manifestation of neuro‐fuzzy simulation environment for prognostication of water absorption kinetics of soybean grains in thermo‐ultrasonication‐assisted soaking process

Shafaei

Nourmohamadi-Moghadami

Kamgar

2021

J Food Process Engineering

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Neuro‐fuzzy simulation of food engineering phenomena can be a supportive manner for integrated realization of compound trends of involved parameters. This article is aimed to manifest neuro‐fuzzy simulation environment for prognostication of water absorption kinetics of soybean grains in thermo‐ultrasonication‐assisted soaking process. The simulation environment was developed based on various types and numbers of input membership functions, defuzzification methods, types of output membership functions, and numbers of training cycles. Promising performance of the prominent environment guaranteed its reliability for prognostication of water absorption kinetics on the basis of numeral variables of soaking time and temperature (20, 30, 40, 50 and 60°C), and nominal variable of soaking strategy (thermic and ultrasonication [25 kHz and 360 W]). The simulation results divulged that applying both strategies of thermic and ultrasonication (thermo‐ultrasonication strategy) was more effective than individual strategy of thermic and ultrasonication in order to proliferate water absorption into the grains. However, contributory influence of the ultrasonication on water absorption was less apparent at higher soaking temperatures and it was more dominant, when soaking time augmented. Practical Applications It can be asserted that the neuro‐fuzzy simulation environment manifested and verified in this study can be practically utilized in oil extraction and seed priming process, and industrial production of some popular soybean food products (Soymilk, Okara, Tofu, Shoyu, Miso, and Natto).

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confidence: 54%

Manifestation of neuro‐fuzzy simulation environment for prognostication of water absorption kinetics of soybean grains in thermo‐ultrasonication‐assisted soaking process

Shafaei

Nourmohamadi-Moghadami

Kamgar

2021

J Food Process Engineering

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“…They found better performance of the SVR-WOA model with WI = 0.9987, 0.9997, CC = 0.9975, 0.9995, EC = 0.9949, 0.9989, RMSE = 0.0808, 0.0617 mm/month, and MAE = 0.0658, 0.0489 for the Algiers and Tlemcen sites, respectively. Gonzalez del Cerro et al [78] compared the predictive performance of the ANFIS against the radiation and temperature-based empirical models for estimating the daily ET o in Tamil Nadu and the Coimbatore provinces of India. Results reveal that the ANFIS-based model (MAE = 0.0008 mm/day, WI = 0.9999, and CC = 0.9999) with all climatic data, i.e., mean air temperature, relative humidity, wind speed, and solar radiation produce better estimates than the empirical models.…”

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confidence: 99%

Evaluation of Machine Learning versus Empirical Models for Monthly Reference Evapotranspiration Estimation in Uttar Pradesh and Uttarakhand States, India

et al. 2022

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Reference evapotranspiration (ETo) plays an important role in agriculture applications such as irrigation scheduling, crop simulation, water budgeting, and reservoir operations. Therefore, the accurate estimation of ETo is essential for optimal utilization of available water resources on regional and global scales. The present study was conducted to estimate the monthly ETo at Nagina (Uttar Pradesh State) and Pantnagar (Uttarakhand State) stations by employing the three ML (machine learning) techniques including the SVM (support vector machine), M5P (M5P model tree), and RF (random forest) against the three empirical models (i.e., Valiantzas-1: V-1, Valiantzas-2: V-2, Valiantzas-3: V-3). Three different input combinations (i.e., C-1, C-2, C-3) were formulated by using 8-year (2009–2016) climatic data of wind speed (u), solar radiation (Rs), relative humidity (RH), and mean air temperature (T) recorded at both stations. The predictive efficacy of ML and the empirical models was evaluated based on five statistical indicators i.e., CC (correlation coefficient), WI (Willmott index), EC (efficiency coefficient), RMSE (root mean square error), and MAE (mean absolute error) presented through a heatmap along with graphical interpretation (Taylor diagram, time-series, and scatter plots). The results showed that the SVM-1 model corresponding to the C-1 input combination outperformed the other ML and empirical models at both stations. Moreover, the SVM-1 model had the lowest MAE (0.076, 0.047 mm/month) and RMSE (0.110, 0.063 mm/month), and highest EC (0.995, 0.999), CC (0.998, 0.999), and WI (0.999, 1.000) values during validation period at Nagina and Pantnagar stations, respectively, and closely followed by the M5P model. Consequently, the ML model (i.e., SVM) was found to be more robust, and reliable in monthly ETo estimation and can be used as a promising alternative to empirical models at both study locations.

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“…studies(Cerro et al, 2021;Elijah et al, 2020;Kaveh et al, 2018; In this study, there was no precise relationship between moisture content and soaking scheme. The neuro-fuzzy simulation system can extract complicated relationship between output and input parameters using both benefits of artificial neural network and fuzzy inference system.…”

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confidence: 84%

Adequacy assessment of neuro‐fuzzy simulation system for characterization of hydration kinetics of sesame seeds subjected to thermic and ultrasonication schemes

Shafaei

Nourmohamadi-Moghadami

Kamgar

2021

J Food Process Preserv

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Neuro-fuzzy simulation of food engineering phenomena can be a supportive manner for integrated realization of compound trends of involved parameters. This paper is aimed to develop neuro-fuzzy simulation system for characterization of hydration kinetics of sesame seeds subjected to thermic and ultrasonication schemes. The simulation system was developed based on various types and numbers of input membership functions, defuzzification methods, types of output membership functions, and numbers of training cycles. Promising performance of prominent system guaranteed its reliability for characterization of hydration kinetics on the basis of numeral variables of soaking time and temperature (20, 30, 40, 50 and 60°C) and nominal variable of soaking scheme (thermic and ultrasonication [25 kHz and 360 W]). The simulation results divulged that applying both schemes of thermic and ultrasonication (thermo-ultrasonication scheme) was more effective than individual scheme of thermic and ultrasonication in order to proliferate water absorption into the grains.However, contributory influence of the ultrasonication on water absorption was less apparent at higher soaking temperatures, and it was more dominant, when soaking time augmented. Practical applicationsIt can be asserted that the neuro-fuzzy simulation system developed and verified in this study can be practically utilized in sesame oil extraction process and industrial production of popular sesame snack food kwon as sesame paste (Tahini). How to cite this article: Shafaei SM, Nourmohamadi-Moghadami A, Kamgar S. Adequacy assessment of neurofuzzy simulation system for characterization of hydration kinetics of sesame seeds subjected to thermic and ultrasonication schemes. J Food Process Preserv.

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Modelling the daily reference evapotranspiration in semi-arid region of South India: A case study comparing ANFIS and empirical models

Cited by 35 publications

References 25 publications

Manifestation of neuro‐fuzzy simulation environment for prognostication of water absorption kinetics of soybean grains in thermo‐ultrasonication‐assisted soaking process

Manifestation of neuro‐fuzzy simulation environment for prognostication of water absorption kinetics of soybean grains in thermo‐ultrasonication‐assisted soaking process

Evaluation of Machine Learning versus Empirical Models for Monthly Reference Evapotranspiration Estimation in Uttar Pradesh and Uttarakhand States, India

Adequacy assessment of neuro‐fuzzy simulation system for characterization of hydration kinetics of sesame seeds subjected to thermic and ultrasonication schemes

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