Automated Mapping using Multilevel B-Splines

Saveliev, Anatoly A.; Romanov, Andrey V.; Mukharamova, S. S.

doi:10.2104/ag050017

Cited by 6 publications

(5 citation statements)

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“…In order to do this the average monthly temperatures at 204 meteorological stations of EPR were calculated based on daily air temperature observations at Roshydromet. Spatial interpolation was done for the entire study region using the Multilevel B-spline Approximation method [46], and "indicator" rasters were built for each month, I(i), i = 1,...,12, where 1 or 0 in pixel indicated i-th month as "warm" or "cold", respectively (Figure 2). Assessment of soil-protective properties of vegetation in the i-th month of the j-th year was carried out separately (by different methods) on arable lands and on all other types of lands (non-arable).…”

Section: C-factor Assessment Methodsmentioning

confidence: 99%

Estimating the Soil Erosion Cover-Management Factor at the European Part of Russia

Mukharamova

Saveliev

Иванов

et al. 2021

IJGI

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Evaluation of the vegetation and agricultural-management factor (C-factor) is an important task, the solution of which affects the correct assessment of the intensity of soil erosion. For the vast area of the European part of Russia (EPR), this task is particularly relevant since no products allow taking into account the C-factor. An approach based on automated interpretation of the main crop groups based on MODIS satellite imaging data from Terra and Aqua satellites with the LSTM machine-learning method was used to achieve this goal. The accuracy of crop group recognition compared to the open data of the Federal State Statistics Service of Russia was 94%. The resulting crop maps were used to calculate the C-factor for each month of a particular year from 2014 to 2019. After that, summaries were made at the regional and landscape levels. The average C-factor value for the EPR was 0.401, for the forest landscape zone 0.262, for the forest-steppe zone 0.362, and for the steppe zone 0.454. The obtained results are in good correlation with the results of previous field studies and provide up-to-date (based on 2014–2019 data) estimates of C-factor for rainfall erosion (monthly, annual) with high spatial detail (250 m).

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Section: C-factor Assessment Methodsmentioning

confidence: 99%

Estimating the Soil Erosion Cover-Management Factor at the European Part of Russia

Mukharamova

Saveliev

Иванов

et al. 2021

IJGI

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“…Therefore, the equipment flexibility can automatically be integrated into the designed surface with the prior knowledge from the mined-out areas. The implementation of multilevel B-splines in this research was due to its high-speed computation (Saveliev et al 2005). Future research could investigate the use of kriging for the interpolation and explore its possible advantages.…”

Section: Discussionmentioning

confidence: 99%

Pilot Point Optimization of Mining Boundaries for Lateritic Metal Deposits: Finding the Trade-off Between Dilution and Ore Loss

et al. 2018

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Geological contacts in lateritic metal deposits (footwall topographies) often delineate the orebody boundaries. Spatial variations seen in such contacts are frequently higher than those for the metal grades of the deposit. Therefore, borehole spacing chosen based on the grade variations cannot adequately capture the geological contact variability. Consequently, models created using such boreholes cause high volumetric uncertainties in the actual and targeted ore extraction volumes, which, in turn, lead to high unplanned dilution and ore losses. In this paper, a method to design optimum ore/mining boundaries for lateritic metal deposits is presented. The proposed approach minimizes the dilution/ore losses and comprises two main steps. First, the uncertainty on the orebody boundary is represented using a set of stochastic realizations generated with a multiple-point statistics algorithm. Then, the optimal orebody boundary is determined using an optimization technique inspired by a model calibration method called Pilot Points. The pilot points represent synthetic elevation values and they are used to construct smooth mining boundaries using the multilevel B-Spline technique. The performance of a generated surface is evaluated using the expected sum of losses in each of the stochastic realizations. The Simulated Annealing algorithm is used to iteratively determine the pilot point values which minimize the expected losses. The results show a significant reduction in the dilution volume as compared to those obtained from the actual mining operation.

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“…While not strictly an exact interpolation method, the MBA algorithm was chosen as it is an extremely fast algorithm. Furthermore, the degree of smoothness and exactness of the method may be precisely controlled, as recalled by Saveliev et al (2005).…”

Section: Residual Interpolationmentioning

confidence: 99%

From research to applications – examples of operational ensemble post-processing in France using machine learning

Taillardat

Mestre

2020

Nonlin. Processes Geophys.

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Abstract. Statistical post-processing of ensemble forecasts, from simple linear regressions to more sophisticated techniques, is now a well-known procedure for correcting biased and poorly dispersed ensemble weather predictions. However, practical applications in national weather services are still in their infancy compared to deterministic post-processing. This paper presents two different applications of ensemble post-processing using machine learning at an industrial scale. The first is a station-based post-processing of surface temperature and subsequent interpolation to a grid in a medium-resolution ensemble system. The second is a gridded post-processing of hourly rainfall amounts in a high-resolution ensemble prediction system. The techniques used rely on quantile regression forests (QRFs) and ensemble copula coupling (ECC), chosen for their robustness and simplicity of training regardless of the variable subject to calibration. Moreover, some variants of classical techniques used, such as QRF and ECC, were developed in order to adjust to operational constraints. A forecast anomaly-based QRF is used for temperature for a better prediction of cold and heat waves. A variant of ECC for hourly rainfall was built, accounting for more realistic longer rainfall accumulations. We show that both forecast quality and forecast value are improved compared to the raw ensemble. Finally, comments about model size and computation time are made.

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Automated Mapping using Multilevel B-Splines

Cited by 6 publications

References 0 publications

Estimating the Soil Erosion Cover-Management Factor at the European Part of Russia

Estimating the Soil Erosion Cover-Management Factor at the European Part of Russia

Pilot Point Optimization of Mining Boundaries for Lateritic Metal Deposits: Finding the Trade-off Between Dilution and Ore Loss

From research to applications – examples of operational ensemble post-processing in France using machine learning

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