Modeling soil bulk density through a complete data scanning procedure: Heuristic alternatives

Shiri, Jalal; Keshavarzi, Ali; Kisi, Özgür; Karimi, Shahram; Iturrarán-Viveros, Ursula

doi:10.1016/j.jhydrol.2017.04.035

Cited by 40 publications

(20 citation statements)

References 43 publications

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“…For example, many techniques were tested to estimate bulk density based on clay and sand content, calcium carbonate equivalent, pH, and organic carbon content, with differing conclusions to the best performing technique of PTF development (Table ). Shiri et al () documented that for this application the novel gene expression programming technique performed strongest, but the differences from the other methods were not significant.…”

Section: Methods To Derive and Evaluate Pedotransfer Functionsmentioning

confidence: 91%

Pedotransfer Functions in Earth System Science: Challenges and Perspectives

Looy

Bouma

Herbst

et al. 2017

Reviews of Geophysics

397

297

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Soil, through its various functions, plays a vital role in the Earth's ecosystems and provides multiple ecosystem services to humanity. Pedotransfer functions (PTFs) are simple to complex knowledge rules that relate available soil information to soil properties and variables that are needed to parameterize soil processes. In this paper, we review the existing PTFs and document the new generation of PTFs developed in the different disciplines of Earth system science. To meet the methodological challenges for a successful application in Earth system modeling, we emphasize that PTF development has to go hand in hand with suitable extrapolation and upscaling techniques such that the PTFs correctly represent the spatial heterogeneity of soils. PTFs should encompass the variability of the estimated soil property or process, in such a way that the estimation of parameters allows for validation and can also confidently provide for extrapolation and upscaling purposes capturing the spatial variation in soils. Most actively pursued recent developments are related to parameterizations of solute transport, heat exchange, soil respiration, and organic carbon content, root density, and vegetation water uptake. Further challenges are to be addressed in parameterization of soil erosivity and land use change impacts at multiple scales. We argue that a comprehensive set of PTFs can be applied throughout a wide range of disciplines of Earth system science, with emphasis on land surface models. Novel sensing techniques provide a true breakthrough for this, yet further improvements are necessary for methods to deal with uncertainty and to validate applications at global scale.

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Section: Methods To Derive and Evaluate Pedotransfer Functionsmentioning

confidence: 91%

Pedotransfer Functions in Earth System Science: Challenges and Perspectives

Looy

Bouma

Herbst

et al. 2017

Reviews of Geophysics

397

297

View full text Add to dashboard Cite

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“…Thus, the outcomes of the Gamma test are confirmed by the GEP. Shiri et al [26] argued that the differences between the input selection of the GEP and Gamma test might be linked to their functional structure, where a major assumption with the Gamma test is that the existing interrelation of the studied problem consisted of a smooth function and a random variable, while GEP develops the structure and constants of the formulas simultaneously. The extent to which site-specific constants are embedded in the formulation-and whether those constants are similar to or different than those at another statio-would dictate the transferability of the function between stations.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, it is necessary to select a chromosomal architecture where a specific head size (= 8) and the number of genes (= 3) are utilized as advised by previous authors (e.g., [26]). Finally, we selected genetic operators.…”

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

“…In this research, identifying the influential independent variable on soil P for selecting the most important input parameters was utilized using the statistical Gamma test. Constructing models based on this calibration test can be considered inflexible, though it may be advantageous in some situations where model selection is integrated together with the variable selection approach [26]. The obtained outputs using this test revealed that the best input composition would be a combination of pH, OC, and clay content, which are used to register the maximum influence on soil P with the minimum Gamma statistic (0.0193).…”

Section: Modeling Protocolmentioning

confidence: 99%

“…The use of the wavelet transform approach might be applied in this context for improving the models' overall performance accuracy. It is interesting to note that the use of AI techniques in this regard is very limited, at least, in semiarid areas and larger scales (e.g., [23,24]) despite their wider applications in various soil and water analysis issues, such as modeling soil cation exchange capacity [8,25], modeling soil bulk density [26], predicting groundwater level fluctuations [27], estimating reference evapotranspiration [28], simulating watershed sediment amount [29], and estimating terrestrial parameters such as solar radiation [30], as well as groundwater pollution studies [31].…”

Section: Introductionmentioning

confidence: 99%

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Estimating Soil Available Phosphorus Content through Coupled Wavelet–Data-Driven Models

et al. 2020

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Soil phosphorus (P) is a vital but limited element which is usually leached from the soil via the drainage process. Soil phosphorus as a soluble substance can be delivered through agricultural fields by runoff or soil loss. It is one of the most essential nutrients that affect the sustainability of crops as well as the energy transfer for living organisms. Therefore, an accurate simulation of soil phosphorus, which is considered as a point source pollutant in elevated contents, must be performed. Considering a crucial issue for a sustainable soil and water management, an effective soil phosphorus assessment in the current research was conducted with the aim of examining the capability of five different wavelet-based data-driven models: gene expression programming (GEP), neural networks (NN), random forest (RF), multivariate adaptive regression spline (MARS), and support vector machine (SVM) in modeling soil phosphorus (P). In order to achieve this goal, several parameters, including soil pH, organic carbon (OC), clay content, and soil P data, were collected from different regions of the Neyshabur plain, Khorasan-e-Razavi Province (Northeast Iran). First, a discrete wavelet transform (DWT) was applied to the pH, OC, and clay as the inputs and their subcomponents were utilized in the applied data-driven techniques. Statistical Gamma test was also used for identifying which effective soil parameter is able to influence soil P. The applied methods were assessed through 10-fold cross-validation scenarios. Our results demonstrated that the wavelet–GEP (WGEP) model outperformed the other models with respect to various validations, such as correlation coefficient (R), scatter index (SI), and Nash–Sutcliffe coefficient (NS) criteria. The GEP model improved the accuracy of the MARS, RF, SVM, and NN models with respect to SI-NS (By comparing the SI values of the GEP model with other models namely MARS, RF, SVM, and NN, the outputs of GEP showed more accuracy by 35%, 30%, 40%, 50%, respectively. Similarly, the results of the GEP outperformed the other models by 3.1%, 2.3%, 4.3%, and 7.6%, comparing their NS values.) by 35%-3.1%, 30%-2.3%, 40%-4.3%, and 50%-7.6%, respectively.

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