RETRACTED ARTICLE: Management of higher heating value sensitivity of biomass by hybrid learning technique

Lakovic, Nadja; Khan, Afrasyab; Petković, Biljana; Petković, Dalibor; Kuzman, Boris; Resic, Sead; Jermsittiparsert, Kittisak; Azam, Sikander

doi:10.1007/s13399-020-01223-w

Cited by 17 publications

(5 citation statements)

References 50 publications

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“…The development of science and technology [10][11][12][13][14][15] provides the possibility for disaster prevention and control. Studying the natural laws of the basin [16,17] and predicting the hydrometeorological elements of the basin are important measures to prevent natural disasters.…”

Section: Introductionmentioning

confidence: 99%

Joint Effects of the DEM Resolution and the Computational Cell Size on the Routing Methods in Hydrological Modelling

Chen

et al. 2022

Water

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Natural disasters, including droughts and floods, have caused huge losses to mankind. Hydrological modelling is an indispensable tool for obtaining a better understanding of hydrological processes. The DEM-based routing methods, which are widely used in the distributed hydrological models, are sensitive to both the DEM resolution and the computational cell size. Too little work has been devoted to the joint effects of DEM resolution and computational cell size on the routing methods. This study aims to study the joint effects of those two factors on discharge simulation performance with two representative routing methods. The selected methods are the improved aggregated network-response function routing method (I-NRF) and the Liner-reservoir-routing method (LRR). Those two routing methods are combined with two runoff generation models to simulate the discharge. The discharge simulation performance is evaluated under the cross combination of four DEM resolutions (i.e., 90 m, 250 m, 500 m, and 1000 m) and fifty-six computational cell sizes (ranging from 5 arc-min to 60 arc-min). Eleven years of hydroclimatic data from the Jianxi basin (2000–2010) and the Shizhenjie basin (1983–1993) in China are used. The results show that the effects of the DEM resolution and the computational cell size are different on the I-NRF method and the LRR method. The computational cell size has nearly no influence on the performance of the I-NRF methods, while the DEM resolution does. On the contrary, the LRR discharge simulation performance decreases with oscillating values as the computational cell size increases, but is hardly affected by the DEM resolution. Furthermore, the joint effects of the DEM resolution and the computational cell size can be ignored for both routing methods. The results of this study will help to establish the appropriate DEM resolution, computational cell size, and routing method when researchers build hydrological models to predict future disasters.

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Section: Introductionmentioning

confidence: 99%

Joint Effects of the DEM Resolution and the Computational Cell Size on the Routing Methods in Hydrological Modelling

Chen

et al. 2022

Water

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“…Complete set of new data, used for this estimation, consisted of 237 examples gathered from the literature on experimentally determined values for FC, VM, ASH and HHV. The literature that is used comprised five papers: (Cordero et al 2001), (Alaba et al 2020), (Conag et al 2019), (Lakovic et al 2021) and (Pattanayak et al 2020), denoted with I, II, III, IV and V, respectively, in the Table 8 where the excerpt of the full data set is given. The complete dataset is available from (Jakšić 2021).…”

Section: Resultsmentioning

confidence: 99%

Comparing artificial neural network algorithms for prediction of higher heating value for different types of biomass

Jakšić

Jakšić²,

Guha³

et al. 2022

Soft Comput

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The paper presents the comparative qualitative and quantitative analysis of twelve algorithms for training artificial neural networks (ANN) which predict the higher heating value (HHV) of biomass based on the proximate analysis (fixed carbon, volatile matter, and ash percentage). The twelve networks, with the same structure but different training algorithm, were fed with 318 experimental data triplets from literature for different biomass species and trained with 318 corresponding HHV values used for the supervised learning. Our comparative analysis showed that several algorithms resulted in ANNs generating outputs well correlated with the true measured values of the biomass HHV. Of those, Levenberg-Marquardt algorithm gives the best results in terms of mean squared error calculated on the training set data, while Bayesian regularization gives the best results in terms of regression. When applied to new datasets, unknown to the ANNs trained here, the highest accuracy of the HHV prediction was obtained by Conjugate Gradient, Powell/Beale Restarts training function. It ensured prediction based on the unknown datasets better than Levenberg-Marquardt algorithm. The described approach can be used for predicting the calorific values of different biomass species, including newly proposed ones, as well as for optimizing the HHV for both pure biomass and biomass mixtures.

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“…Over the last few years, many studies have mentioned that the prosperous applications of AI techniques in various scop of agricultural, water irrigation engineering and water engineering issues, such as 19 , 20 and 21 . Application of Probabilistic Linear Regression (PLR), random forest, and cross-correlation function to study the effective rate of climatic parameters on the potential evapotranspiration of barley through the growing season was reported by 22 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of Water Distribution Uniformity of Sprinkler Irrigation System Based on Machine Learning Algorithms

Elhussiny,

Hassan,

Habssa

et al. 2023

Preprint

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The coefficients of uniformity (Christiansen's uniformity coefficient CU and distribution uniformity DU), an important parameter for designing irrigation systems, and is an accurate measure for water lose. That research include, three sprinklers were used to measure the CU and DU results based on operating pressure, heights of sprinkler, discharge, nozzle diameter, wind speed, humidity, highest temperature and lowest temperature to determine the best parameters for good irrigation design and also to select the best scenario effects on CU and DU values for square and triangular system based on four scenarios were applied to predict the DU based on machine learning algorithms. The main findings are 1) the highest CU was 86.7% in the square system of 2520 sprinkler under 200 kPa, height of 0.5m and the discharge was 0.855 m3/h (Nozzle 2.5mm). Furthermore, it was 87.3% in the triangled system under the same pressure and discharge however on the 1m height. 2) In CU, the highest of R2 is 0.796, 0.825 and 0.929 in RF, XGB and XGB-RF respectively in the first scenario. 3) On contrast, for the DU, the highest value of R2 is 0.7014, 0.4791 and 0.8275 in RF, XGB and XGB-RF respectively in the first scenario.

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RETRACTED ARTICLE: Management of higher heating value sensitivity of biomass by hybrid learning technique

Cited by 17 publications

References 50 publications

Joint Effects of the DEM Resolution and the Computational Cell Size on the Routing Methods in Hydrological Modelling

Joint Effects of the DEM Resolution and the Computational Cell Size on the Routing Methods in Hydrological Modelling

Comparing artificial neural network algorithms for prediction of higher heating value for different types of biomass

Prediction of Water Distribution Uniformity of Sprinkler Irrigation System Based on Machine Learning Algorithms

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