Application of novel data mining algorithms in prediction of discharge and end depth in trapezoidal sections

Khosravinia, Payam; Nikpour, Mohammad Reza; Kişi, Özgür; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1016/j.compag.2020.105283

Cited by 14 publications

(5 citation statements)

References 52 publications

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“…The ET o is a key factor in the hydrological process used for the calculation of the water requirement, based on the meteorological parameters [25] and quantification in agriculture and precision farming [26]. In addition to empirical ET o models, newly emerging artificial intelligence and machine learning techniques have recently been used to estimate ET o [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

“…Intelligent computer models, such as the artificial neural network (ANN) methodology have been created as alternate ways for calculating ET throughout the last decade [31]. Because of its broad use in a variety of scientific fields, ANNs have shown significant progress in the studies on hydrology and water resources [27,28]. Artificial neural networks are large, parallel-distributed processors made up of basic processing units that have a natural proclivity for storing and making available experimental information.…”

Section: Introductionmentioning

confidence: 99%

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Combination of Limited Meteorological Data for Predicting Reference Crop Evapotranspiration Using Artificial Neural Network Method

et al. 2022

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Reference crop evapotranspiration (ETo) is an important component of the hydrological cycle that is used for water resource planning, irrigation, and agricultural management, as well as in other hydrological processes. The aim of this study was to estimate the ETo based on limited meteorological data using an artificial neural network (ANN) method. The daily data of minimum temperature (Tmin), maximum temperature (Tmax), mean temperature (Tmean), solar radiation (SR), humidity (H), wind speed (WS), sunshine hours (Ssh), maximum global radiation (gradmax), minimum global radiation (gradmin), day length, and ETo data were obtained over the long-term period from 1969 to 2019. The analysed data were divided into two parts from 1969 to 2007 and from 2008 to 2019 for model training and testing, respectively. The optimal ANN for forecasting ETo included Tmax, Tmin, H, and SR at hidden layers (4, 3); gradmin, SR, and WS at (6, 4); SR, day length, Ssh, and Tmean at (3, 2); all collected parameters at hidden layer (5, 4). The results showed different alternative methods for estimation of ETo in case of a lack of climate data with high performance. Models using ANN can help promote the decision-making for water managers, designers, and development planners.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combination of Limited Meteorological Data for Predicting Reference Crop Evapotranspiration Using Artificial Neural Network Method

et al. 2022

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“…Hence, empirical formulation is demonstrated as a remarkable limitation on the ET0 estimation. During the last few decades, models based on computer aid capacity indicated a distinguished progress in the hydrology and water resources fields [9][10][11][12][13]. Artificial intelligence (AI) models have been extensively applied as a reliable soft computing technology for ET0 estimation based on the available and measured climatic variables [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Adnan

Heddam

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2020

Sustainability

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The potential or reference evapotranspiration (ET0) is considered as one of the fundamental variables for irrigation management, agricultural planning, and modeling different hydrological pr°Cesses, and therefore, its accurate prediction is highly essential. The study validates the feasibility of new temperature based heuristic models (i.e., group method of data handling neural network (GMDHNN), multivariate adaptive regression spline (MARS), and M5 model tree (M5Tree)) for estimating monthly ET0. The outcomes of the newly developed models are compared with empirical formulations including Hargreaves-Samani (HS), calibrated HS, and Stephens-Stewart (SS) models based on mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency. Monthly maximum and minimum temperatures (Tmax and Tmin) observed at two stations in Turkey are utilized as inputs for model development. In the applications, three data division scenarios are utilized and the effect of periodicity component (PC) on models’ accuracies are also examined. By importing PC into the model inputs, the RMSE accuracy of GMDHNN, MARS, and M5Tree models increased by 1.4%, 8%, and 6% in one station, respectively. The GMDHNN model with periodic input provides a superior performance to the other alternatives in both stations. The recommended model reduced the average error of MARS, M5Tree, HS, CHS, and SS models with respect to RMSE by 3.7–6.4%, 10.7–3.9%, 76–75%, 10–35%, and 0.8–17% in estimating monthly ET0, respectively. The HS model provides the worst accuracy while the calibrated version significantly improves its accuracy. The GMDHNN, MARS, M5Tree, SS, and CHS models are also compared in estimating monthly mean ET0. The GMDHNN generally gave the best accuracy while the CHS provides considerably over/under-estimations. The study indicated that the only one data splitting scenario may mislead the modeler and for better validation of the heuristic methods, more data splitting scenarios should be applied.

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“…Soft computing, which is sometimes referred to as computational intelligence, provides solutions for problems that would otherwise be unsolvable or difficult and time‐consuming to solve or for which there are no effective computational algorithms (Yaseen et al, 2018; Tiyasha Tung & Yaseen, 2020). Several scholars have described how soft computing approaches, such as support vector machines, artificial neural networks, adaptive neuro‐fuzzy inference systems, and Gaussian process regression (GPR), can be used to solve various engineering problems (Aggarwal et al, 2013; Khosravi et al, 2018; Li et al, 2018; Salih et al, 2019; Yousif et al, 2019; Khosravinia et al, 2020; Mohammed et al, 2020). Over the past literature, several researchers have recommended that such approaches be applied to infiltration problems (Sy, 2006; Rahmati, 2017; Tiwari et al, 2017; Sihag et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Modelling infiltration rates in permeable stormwater channels using soft computing techniques*

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Sihag

Yusuf

et al. 2020

Irrigation and Drainage

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In the design of permeable stormwater channels, the ability to quantify infiltration rates accurately is important for assessing the capability of such channels to perform their required functions. Most of the available infiltration models neglect the effects of water level and channel section on the infiltration rate. In this study, physical channel models, with different channel sections, were developed in the laboratory and used to measure the infiltration rates. The performance of three soft computing techniques, including Gaussian process regression, M5P, and random forest (RF) models, were evaluated against measured values. Seven independent input variables, namely, channel side slope (m), base width (b), water level (y), sand (%), silt (%), clay (%), and time (T) and the output variable infiltration rate (f(t)), were considered in the model development and validation. The Gaussian progression–Pearson VII universal kernel function model approach was found to perform best for the data set considered, followed by the RF‐based model. The sensitivity investigation showed that time, water level, and channel side slope were the most influential input variables in predicting infiltration rates for permeable stormwater channels and should be given primary consideration in designing such channels. © 2020 John Wiley & Sons, Ltd.

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Application of novel data mining algorithms in prediction of discharge and end depth in trapezoidal sections

Cited by 14 publications

References 52 publications

Combination of Limited Meteorological Data for Predicting Reference Crop Evapotranspiration Using Artificial Neural Network Method

Combination of Limited Meteorological Data for Predicting Reference Crop Evapotranspiration Using Artificial Neural Network Method

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Modelling infiltration rates in permeable stormwater channels using soft computing techniques*

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