Application of non-conventional soft computing approaches for estimation of reference evapotranspiration in various climatic regions

Raza, Ali; Shoaib, Muhammad; Khan, Afzaal; Baig, Faisal; Faiz, Muhammad Abrar; Khan, Mudasser Muneer

doi:10.1007/s00704-019-03007-3

Cited by 18 publications

(12 citation statements)

References 29 publications

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“…Several factors including hidden/output layer, weights and neurons, activation functions, network and other tuning parameters helped in determining suitable model. These factors are explicitly explained in Raza et al [17][18]. It was noted that these models produced good results using fewer inputs which resembled with field calculations.…”

Section: Development Of Soft Computing Modelsmentioning

confidence: 97%

“…The structure of soft computing model depends upon its input/output dataset, neurons and activation [17], neurons in input/hidden/output layers accounted preeminent factor for determining structure of model. Alternatively, the structure in tree based soft computing models could be determined by considering its depth, size and level [18].…”

Section: Structurementioning

confidence: 99%

“…The creation of a separate ET o model with help of soft computing approaches for each station is another important matter. In addition, the development of a generalized ET o model for the accurate ET o estimation in all stations within an area has been considered by a small number of studies such as [17,18,44]. This becomes significant in case of developing countries where climatic data of mostly stations is missing or not available due to technical issues.…”

Section: Reviewed Articlesmentioning

confidence: 99%

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A Systematic Review on Estimation of Reference Evapotranspiration under Prisma Guidelines

Raza¹,

Hu²,

Shoaib³

et al. 2021

Pol. J. Environ. Stud.

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IntroductionMitigation of water shortage has become a critical issue of the 21 st century and labeled as blue gold. The agriculture sector is ranked as the highest consumptive user of water in most of the countries. The water withdrawal from the total amount of water in developing counties is estimated nearly 81% while it is accounted for 71% globally. In addition, more than 55% of all the world's fresh water withdrawals are allocated towards irrigation use [1]. Reference evapotranspiration (ET o ) is the principal component of the global hydrological cycle which affects irrigation, water requirement and crops yield. It consists of evaporation and transpiration processes. The phenomenon in which water moves from the land surface to the atmosphere is called evaporation while transpiration is the process in which plant roots

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Section: Development Of Soft Computing Modelsmentioning

confidence: 97%

Section: Structurementioning

confidence: 99%

Section: Reviewed Articlesmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Review on Estimation of Reference Evapotranspiration under Prisma Guidelines

Raza¹,

Hu²,

Shoaib³

et al. 2021

Pol. J. Environ. Stud.

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“…(IG) can be defined as "assessing the anticipated decrease in entropy" or "evaluating the clarity of a training set." The following equations [37] show the generic empirical formula of E and IG:…”

Section: Single Decision Tree (Sdt)mentioning

confidence: 99%

Application of Machine Learning Techniques in Rainfall–Runoff Modelling of the Soan River Basin, Pakistan

Khan

Shoaib

Hammad

et al. 2021

Water

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Rainfall–runoff modelling has been at the essence of research in hydrology for a long time. Every modern technique found its way to uncover the dynamics of rainfall–runoff relation for different basins of the world. Different techniques of machine learning have been extensively applied to understand this hydrological phenomenon. However, the literature is still scarce in cases of extensive research work on the comparison of streamline machine learning (ML) techniques and impact of wavelet pre-processing on their performance. Therefore, this study compares the performance of single decision tree (SDT), tree boost (TB), decision tree forest (DTF), multilayer perceptron (MLP), and gene expression programming (GEP) in rainfall–runoff modelling of the Soan River basin, Pakistan. Additionally, the impact of wavelet pre-processing through maximal overlap discrete wavelet transformation (MODWT) on the model performance has been assessed. Through a comprehensive comparative analysis of 110 model settings, we concluded that the MODWT-based DTF model has yielded higher Nash–Sutcliffe efficiency (NSE) of 0.90 at lag order (Lo4). The coefficient of determination for the model was also highest among all the models while least root mean square error (RMSE) value of 23.79 m3/s was also produced by MODWT-DTF at Lo4. The study also draws inter-technique comparison of the model performance as well as intra-technique differentiation of modelling accuracy.

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“…In recent years, however, artificial intelligence-based methods such as the neural networks (Kisi et al 2015;Gavili et al 2018), the support vector machine (Tabari et al 2013), the extreme learning machine (Abdullah et al 2015), decision tree (Huang et al 2019;Raza et al 2020), and hybrid methods (Ehteram et al 2019;Shiri et al 2020;Tikhamarine et al 2019;Zhu et al 2020;Kim et al 2014;Sanikhani et al 2019;Mehdizadeh et al 2017) have had many applications in estimating ET Ref , but among them, multivariate linear regression method has been compared with other empirical equations and soft computing, validated by many researchers (Reis et al 2019;Kisi and Heddam 2019;Mattar and Alazba 2019;Tabari et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of multivariate linear regression for reference evapotranspiration modeling in different climates of Iran

Sharafi

Ghaleni

2021

Theor Appl Climatol

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The study aimed to evaluate the accuracy of empirical equations (Hargreaves-Samani; HS, Irmak; IR and Dalton; DT) and multivariate linear regression models (MLR1–6) for estimating reference evapotranspiration (ETRef) in different climates of Iran based on the Köppen method including arid desert (Bw), semiarid (Bs), humid with mild winters (C), and humid with severe winters (D). For this purpose, climatic data of 33 meteorological stations during 30 statistical years 1990–2019 were used with a monthly time step. Based on various meteorological data (minimum and maximum temperature, relative humidity, wind speed, solar radiation, extraterrestrial radiation, and vapor pressure deficit), in addition to 6 multivariate linear regression models and three empirical equations were used as MLR1, MLR2, and HS (temperature-based), MLR3 and IR (radiation-based), MLR4, MLR5 and DT (mass transfer-based), and MLR6 (combination-based) were also used to estimate the reference evapotranspiration. The results of these models were compared using the root mean square error (RMSE), mean absolute error (MAE), scatter index (SI), determination coefficient (R2), and Nash-Sutcliffe efficiency (NSE) statistical criteria with the evapotranspiration results of the FAO56 Penman-Monteith reference as target data. All MLR models gave better results than empirical equations. The results showed that the simplest regression model (MLR1) based on the minimum and maximum temperature data was more accurate than the empirical equations. The lowest and highest accuracy related to the MLR6 model and HS empirical equation with RMSE was 10.8–15.1 mm month−1 and 22–28.3 mm month−1, respectively. Also, among all the evaluated equations, radiation-based models such as IR in Bw and Bs climates with MAE = 8.01–11.2 mm month−1 had higher accuracy than C and D climates with MAE = 13.44–14.48 mm month−1. In general, the results showed that the ability of regression models was excellent in all climates from Bw to D based on SI < 0.2.

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Application of non-conventional soft computing approaches for estimation of reference evapotranspiration in various climatic regions

Cited by 18 publications

References 29 publications

A Systematic Review on Estimation of Reference Evapotranspiration under Prisma Guidelines

A Systematic Review on Estimation of Reference Evapotranspiration under Prisma Guidelines

Application of Machine Learning Techniques in Rainfall–Runoff Modelling of the Soan River Basin, Pakistan

Evaluation of multivariate linear regression for reference evapotranspiration modeling in different climates of Iran

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