Impact of Infiltration Process Modeling on Soil Water Content Simulations for Irrigation Management

Feki, M. Mnif; Ravazzani, Giovanni; Ceppi, Alessandro; Milleo, Giuseppe; Mancini, Marco

doi:10.3390/w10070850

Cited by 27 publications

(20 citation statements)

References 48 publications

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“…The growth and regression of soil moisture directly affects water consumption and growth of crops. It is an important indicator for drought resistance, flood control [12, 13], and precision irrigation decisions[14,15] in agricultural production. It is important to achieve accurate prediction of soil water regression regular patterns to properly manage agricultural water resources and promote crop yield increases.…”

Section: Introductionmentioning

confidence: 99%

Research on soil moisture prediction model based on deep learning

et al. 2019

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Soil moisture is one of the main factors in agricultural production and hydrological cycles, and its precise prediction is important for the rational use and management of water resources. However, soil moisture involves complex structural characteristics and meteorological factors, and it is difficult to establish an ideal mathematical model for soil moisture prediction. Existing prediction models have problems such as prediction accuracy, generalization, and multi-feature processing capability, and prediction performance must improve. Based on this, taking the Beijing area as the research object, the deep learning regression network (DNNR) with big data fitting capability was proposed to construct a soil moisture prediction model. By integrating the dataset, analyzing the time series of the predictive variables, and clarifying the relationship between features and predictive variables through the Taylor diagram, selected meteorological parameters can provide effective weights for moisture prediction. Test results prove that the deep learning model is feasible and effective for soil moisture prediction. Its’ good data fitting and generalization capability can enrich the input characteristics while ensuring high accuracy in predicting the trends and values of soil moisture data and provides an effective theoretical basis for water-saving irrigation and drought control.

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Section: Introductionmentioning

confidence: 99%

Research on soil moisture prediction model based on deep learning

et al. 2019

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“…They proposed a modified root water uptake function that played a significant role in optimizing the simulation of water and heat fluxes. Feki et al [24] assessed the impact of four infiltration models (SCS-Curve Number, Green and Ampt, Philip and Ross) on soil water content simulations, simulated by the model FEST-WB (flash flood event-based spatially-distributed rainfall-runoff transformations-water balance). They found the Ross solution to be better in its performance through soil water content monitoring.…”

Section: Model Calibration and Water Balancementioning

confidence: 99%

Special Issue “Soil Hydrology in Agriculture”

Basile

Coppola

2019

Water

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“…SCS-CN characterises the natural processes of stormwater runoff by considering the surface and its hydrologic conditions [69]. The SCS-CN model is one of the most popular runoff models and has been constantly developed and improved in many studies [73,[85][86][87]. The basic formula in SCS-CN for calculating the runoff depth (Q) is expressed by Equation 2…”

Section: Effective Precipitationmentioning

confidence: 99%

The Development of a Novel Decision Support System for the Location of Green Infrastructure for Stormwater Management

Kazak

Chruściński²,

Szewrański

2018

Sustainability

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In order to maximise the benefits of green infrastructure in a city’s structure for urban adaptation to climate change, there is a need to support decision-makers in the urban design domain with adequate information that would help them to locate such green infrastructure in the most suitable places. Therefore, the aim of this study was to develop a novel decision support system (DSS) for the location of green infrastructure. The goal of the designed solution is to inform users about the location of urban hydrological sinks, which gather stormwater in urban watersheds, and the amount of water which could accumulate in each location depending on the defined precipitation and the soil’s moisture conditions. The designed DSS is based on a multicomponent methodology including both atmospheric and soil conditions. The DSS was tested using a sample that presents the results of stormwater accumulation calculations. The obtained results show which green areas are the most suitable locations for green infrastructure solutions and which facility is optimal because of its retention abilities and amount of accumulated stormwater. The application of the designed DSS allows us to maximise the benefits of the implementation of green infrastructure within the existing urban land use. The fully editable component of hydrological conditions allows for testing projections of the potential amount of accumulated water in different precipitation scenarios. The study provides a DSS for use by local authorities which enables them to concentrate actions in order to better adapt cities to climate change and environmental extremes.

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Impact of Infiltration Process Modeling on Soil Water Content Simulations for Irrigation Management

Cited by 27 publications

References 48 publications

Research on soil moisture prediction model based on deep learning

Research on soil moisture prediction model based on deep learning

Special Issue “Soil Hydrology in Agriculture”

The Development of a Novel Decision Support System for the Location of Green Infrastructure for Stormwater Management

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