Numerical simulation of irrigation scheduling using fractional Richards equation

Romashchenko, М. I.; Bohaienko, Vsevolod; Matіash, T. V.; Kovalchuk, Volodymyr; Krucheniuk, A. V.

doi:10.1007/s00271-021-00725-3

Cited by 14 publications

(17 citation statements)

References 30 publications

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“…In this context, the paper is a continuation of previous authors' results on high-performance computing in, particularly fractional-order, modelling of mass and moisture transport [7,8,9] studying the performance of multithreaded parallel implementation of finite-difference solver for two-dimensional spacefractional generalization of Richards equation.…”

Section: Introductionmentioning

confidence: 90%

“…The space-fractional generalization of Richards equation stated in terms of water heads and derived similarly to the one-dimensional equation in [7] has the form…”

Section: Mathematical Model and Numerical Methodsmentioning

confidence: 99%

“…The used approach is described in more detail in [7,9]. The numerical solution of the initial-boundary value problem for the model based on Equations (1), ( 2) is performed according to the implicit finite-difference Crank-Nicholson scheme [13] on a uniform grid ω = (x i = ih x , z k = kh z , t j = jτ ) : i = 0, m, k = 0, n, j = 0, 1, 2, ... , (3) where h x = L x /m, h z = L z /n are the steps with respect to the space variables, τ is the step with respect to the time variable.…”

Section: Mathematical Model and Numerical Methodsmentioning

confidence: 99%

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Multithreading Performance Simulating Fractional-Order Moisture Transport on Amd Epyc

Bohaienko

Gladky

2022

JNAM

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The paper studies the performance of multithreaded parallel implementation of a finite-difference solver for a two-dimensional space-fractional generalization of Richards equation. For numerical solution we used implicit Crank-Nicholson scheme with L1-approximation of Caputo fractional derivative and TFQMR linear systems’ solver. OpenMP implementation was tested on three CPUs — server Intel Xeon Bronze 3104 and AMD EPYC 7542 along with laptop AMD Ryzen 3 5300U. Testing results show that the proposed implementation can give close-to-linear acceleration when executing on up to 8 cores. On high-performance AMD EPYC maximal acceleration was achieved when 32-64 cores were used showing limited scalability of the algorithms on such a CPU.

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Section: Introductionmentioning

confidence: 90%

“…The space-fractional generalization of Richards equation stated in terms of water heads and derived similarly to the one-dimensional equation in [7] has the form…”

Section: Mathematical Model and Numerical Methodsmentioning

confidence: 99%

Section: Mathematical Model and Numerical Methodsmentioning

confidence: 99%

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Multithreading Performance Simulating Fractional-Order Moisture Transport on Amd Epyc

Bohaienko

Gladky

2022

JNAM

Self Cite

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“…23 A fractional differential generalization of the one-dimensional Richards equation was used to predict soil water content and the model was calibrated using a particle swarm algorithm. 24 Parameters such as temperature and humidity were selected to predict soil water content using a gray neural network. 25 A nonlinear model for soil water content prediction with fewer training factors is constructed with an extreme learning machine (ELM).…”

Section: Introductionmentioning

confidence: 99%

Predicting soil moisture content of tea plantation using support vector machine optimized by arithmetic optimization algorithm

Yin

Wang

Huang

2023

Journal of Algorithms & Computational Technology

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Soil moisture content (SMC) is an important parameter that affects tea growth. Reasonable soil moisture content improves tea quality and ensures tea yield. Therefore, it is necessary to regularly monitor the soil water content. However, the traditional soil moisture content prediction algorithm has the problems of low accuracy and low efficiency. This paper constructs and evaluates the performance of a hybrid arithmetic optimization algorithm (AOA) and support vector machine (SVM) prediction model (AOA-SVM) for predicting SMC in tea plantations. Grey relation analysis (GRA) and Pearson correlation analysis are adopted to select features for soil moisture content prediction model, and then the correlation of SMC with soil temperature (ST), atmospheric temperature (AT), and soil electrical conductivity (SEC) was analyzed. The optimal penalty parameter ( c) and the parameter of the kernel function ( g) of SVM model are determined by AOA. The mean square error (MSE) and coefficient of determination ([Formula: see text]), mean absolute error (MAE), and mean error (ME) were calculated to evaluate the performance of the model. Meanwhile, the performance of AOA-SVM model is compared with SVM optimized by the sparrow search algorithm (SSA-SVM), extreme learning machine (ELM), support vector machine (SVM), and convolutional neural networks (CNN). The results showed that AOA-SVM, SSA-SVM and CNN are better, with the coefficient of determination above 93%, the coefficient of determination of SVM and ELM is 81.69% and 89.61%, respectively. The AOA-SVM model has the best R2 of 95.03%. This indicates that the AOA-SVM model has significant performance with higher R2, smaller MSE than other models, which has potential implications in precision agriculture.

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“…A quantitative estimation model of soil moisture content was built using the relationship between the ratio spectral index (RSI), difference spectral index (DI) and normalised difference spectral index (NDSI) [25]. The soil water content was predicted using a fractional differential generalisation of the one‐dimensional Richards equation, and the model was adjusted using a meta‐heuristic particle swarm algorithm [26].…”

Section: Introductionmentioning

confidence: 99%

Soil moisture content prediction model for tea plantations based on SVM optimised by the bald eagle search algorithm

Huang

Jiang

Wang

et al. 2021

Cognitive Comp and Systems

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In order to solve the problem of low accuracy and efficiency of soil moisture content prediction in tea plantations and improve the level of soil water content prediction, a soil moisture content prediction model for tea plantations based on the support vector machine (SVM)-optimised bald eagle search (BES) algorithm (BES-SVM) is proposed. Soil data and environmental data of tea plantations were transmitted to the server using sensor nodes and weather station nodes. The prediction models of soil moisture content and natural environmental parameters such as soil electrical conductivity, soil temperature, air temperature, air humidity, light intensity, and rainfall were developed using the SVM model optimised by the bald eagle search algorithm, and the mean square error (MSE) and coefficient of determination (R 2 ) were calculated to evaluate the model performance. Meanwhile, the performance of the BES-SVM model is compared with the particle swarm algorithm optimisation SVM (PSO-SVM) and genetic algorithm optimised SVM (GA-SVM) models. Results show that the proposed model has a mean coefficient of determination of 95.65%, and the prediction performance is better than the PSO-SVM and GA-SVM model, indicating that the BES-SVM model has good performance and is a feasible prediction method for soil water content prediction and guiding irrigation and fertilisation management in tea plantations. K E Y W O R D Sbald eagle search algorithm, precision irrigation, soil moisture content, support vector machine This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Numerical simulation of irrigation scheduling using fractional Richards equation

Cited by 14 publications

References 30 publications

Multithreading Performance Simulating Fractional-Order Moisture Transport on Amd Epyc

Multithreading Performance Simulating Fractional-Order Moisture Transport on Amd Epyc

Predicting soil moisture content of tea plantation using support vector machine optimized by arithmetic optimization algorithm

Soil moisture content prediction model for tea plantations based on SVM optimised by the bald eagle search algorithm

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