Modeling the sprinkler water distribution uniformity by data-driven methods based on effective variables

Maroufpoor, Saman; Shiri, Jalal; Maroufpoor, Eisa

doi:10.1016/j.agwat.2019.01.008

Cited by 27 publications

(14 citation statements)

References 54 publications

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“…Different studies have tried using hybridized AI models for groundwater quality simulation; among the studied AI models are the nature-inspired optimization algorithms like particle swarm optimization, differential evolution, genetic algorithm, ant colony algorithm, firefly algorithm, etc. [44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

Shiri

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2021

PLoS ONE

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Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resources is very crucial as it might affect its applicability from agricultural, drinking, and industrial aspects. Although geo-statistics methods can provide insight about spatial distribution of quality factors, applications of advanced artificial intelligence (AI) models can contribute to produce more accurate results as robust alternative for such a complex geo-science problem. The present research investigates the capacity of several types of AI models for modeling four key water quality variables namely electrical conductivity (EC), sodium adsorption ratio (SAR), total dissolved solid (TDS) and Sulfate (SO4) using dataset obtained from 90 wells in Tabriz Plain, Iran; assessed by k-fold testing. Two different modeling scenarios were established to make simulations using other quality parameters and the geographical information. The obtained results confirmed the capabilities of the AI models for modeling the well groundwater quality variables. Among all the applied AI models, the developed hybrid support vector machine-firefly algorithm (SVM-FFA) model achieved the best predictability performance for both investigated scenarios. The introduced computer aid methodology provided a reliable technology for groundwater monitoring and assessment.

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Section: Introductionmentioning

confidence: 99%

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

Shiri

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2021

PLoS ONE

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“…Distribution efficiency is assessed by distribution uniformity (Ud), which consists of a measure of the capacity of an irrigation system to apply the same amount of water to the entire irrigated area (Baum, Dukes, & Miller, 2005;Mohamed, Peters, Zhu, & Sarwar, 2019). Ed is an important factor in designing and managing irrigation systems, as it is directly related to crop yield and water use efficiency (Clemmens, 1991;Maroufpoor, Shiri, & Maroufpoor, 2019;Mohamed et al, 2019). Uniformity of production when water is the only limiting factor is a function of Ud of the water in the root zone (López-Mata, Tarjuelo, Juan, Ballesteros, & Domínguez, 2010).…”

mentioning

confidence: 99%

Alternative low-cost precipitation kit for assessing irrigation systems

Filgueiras

Cunha

Venâncio

et al. 2020

Semina: Ciênc. Agrár.

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“…In an application on control of grinding processes, Mukherjee & Routroy (2012) analyzed the algorithms BFGS and LM and concluded that the first converges faster and is more accurate. Maroufpoor et al (2019) compared GDM, SCG and LM. They concluded that LM is the most suitable to deal with the modeling of uniform water distribution.…”

Section: Training Algorithmsmentioning

confidence: 99%

Comparison of artificial neural networks learning methods to evaluate supply chain performance

Lunardi

Lima

2021

Gest. Prod.

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The supply chain performance evaluation is a critical activity to continuously improve operations. Literature presents several performance evaluation systems based on multi-criteria methods and artificial intelligence. Among them, the systems based on artificial neural networks (ANN) excel due to their capacity of modeling non-linear relationships between metrics and allowing adaptations to a specific environment by means of historical performance data. These systems’ accuracy depend directly on the adopted training algorithm, and no studies have been found that assess the efficiency of these algorithms when applied to supply chain performance evaluation. In this context, the present study evaluates four ANNs learning methods in order to investigate which one is the most adequate to deal with supply chain evaluation. The algorithms tested were Gradient Descendent Momentum, Levenberg-Marquardt, Quasi-Newton and Scale Conjugate Gradient. The performance metrics were extracted from SCOR®, which is a reference model used worldwide. The random sub-sampling cross-validation method was adopted to find the most adequate topological configuration for each model. A set of 80 topologies was implemented using MATLAB®. The prediction accuracy evaluation was based on the mean square error. For the four level 1 metrics considered, the Levenberg-Marquardt algorithm provided the most precise results. The results of correlation analysis and hypothesis tests reinforce the accuracy of the proposed models. Furthermore, the proposed computational models reached a prediction accuracy higher than previous approaches.

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Modeling the sprinkler water distribution uniformity by data-driven methods based on effective variables

Cited by 27 publications

References 54 publications

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

Alternative low-cost precipitation kit for assessing irrigation systems

Comparison of artificial neural networks learning methods to evaluate supply chain performance

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