Mathematics of Planet Earth

Pardo‐Igúzquiza, Eulogio; Guardiola‐Albert, Carolina; Heredia, Javier; Moreno-Merino, Luis; Durán, Juan José; Vargas-Guzmán, Jose Antonio

doi:10.1007/978-3-642-32408-6

Cited by 14 publications

(1 citation statement)

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“…While a geometrical fractal can be created using mathematical operations (Mandelbrot, Ness 1968), a fractal character of natural cave systems is weakly predictable (Andreychouk et al 2013, Curl 1986, since underlying processes of creation are stochastic (Havlin, Ben-Avraham 2002, Pardo-Igúzquiza et al 2014. From this results a need for quantitative characterization of such geomorphological random phenomena.…”

Section: Introductionmentioning

confidence: 99%

Quantitative estimation of 3D cave networks complexity using random walk analysis

Błachowicz

Andreychouk

2015

Landform Analysis

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The paper presents a new method of quantitative parameterization of volumetric-net geomorphological structures with the use of random walk formalism and an analysis of self-similarity exponent distribution derived from random walk experiments. As examples, two American three-dimensional Wind and Lechuguilla cave networks were elaborated. The provided methodology is able to uniquely characterize the morphology of cave systems.

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Section: Introductionmentioning

confidence: 99%

Quantitative estimation of 3D cave networks complexity using random walk analysis

Błachowicz

Andreychouk

2015

Landform Analysis

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Self-similarity and multiplicative cascade models

Agterberg¹

2015

J. S. Afr. Inst. Min. Metall.

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Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN

et al. 2020

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Rapid and accurate evaluation of cultivated land quality (CLQ) using remotely sensed images plays an important role for national food security and social stability. Current approaches for evaluating CLQ do not consider spectral response relationships between CLQ and spectral indicators based on crop growth stages. This study aimed to propose an accurate spectral model to evaluate CLQ based on late rice phenology. In order to increase the accuracy of evaluation, the Empirical Bayes Kriging (EBK) interpolation was first performed to scale down gross primary production (GPP) products from a 500 m spatial resolution to 30 m. As an indicator, the ability of MODIS-GPPs from critical growth stages (tillering, jointing, heading, and maturity stages) was then investigated by combining Pearson correlation analysis and variance inflation factor (VIF) to select the phases of CLQ evaluation. Finally, a linear Partial Least Squares Regression (PLSR) and two nonlinear models, including Support Vector Regression (SVR) and Genetic Algorithm-Based Back Propagation Neural Network (GA-BPNN), were driven to develop an accurate spectral model of evaluating CLQ based on MODIS-GPPs. The models were tested and compared in the Conghua and Zengcheng districts of Guangzhou City, Guangdong, China. The results showed that based on field measured GPP data, the validation accuracy of 30 m spatial resolution MODIS GPP products with a root mean square error (RMSE) of 7.43 and normalized RMSE (NRMSE) of 1.59% was higher than that of the 500 m MODIS GPP products, indicating that the downscaled 30 m MODIS GPP products by EBK were more appropriate than the 500 m products. Compared with PLSR (R2 = 0.38 and RMSE = 87.97) and SVR (R2 = 0.64 and RMSE = 64.38), the GA-BPNN model (R2 = 0.69 and RMSE = 60.12) was more accurate to evaluate CLQ, implying a non-linear relationship of CLQ with the GPP spectral indicator. This is the first study to improve the accuracy of estimating CLQ using the rice growth stage GPP-driven spectral model by GA-BPNN and can thus advance the literature in this field.

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Mathematics of Planet Earth

Cited by 14 publications

References 0 publications

Quantitative estimation of 3D cave networks complexity using random walk analysis

Quantitative estimation of 3D cave networks complexity using random walk analysis

Self-similarity and multiplicative cascade models

Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN

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