An approach for prediction of shoreline with spatial uncertainty mapping (SLiP-SUM)

San, Bekir Taner; Ulusar, Umit Deniz

doi:10.1016/j.jag.2018.08.005

Cited by 10 publications

(6 citation statements)

References 26 publications

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“…This type of forecasting method has been employed 30 times in the reviewed literature. The simplest method of a forecast in this category is to fit a linear trend to a time series and calculate future values from the resulting regression equation [119,160,163,164,178,179]. More sophisticated methods of time-series forecasting consider auto-correlation, global trends, and seasonal cycles [17].…”

Section: Categorization Of Forecasting Methodsmentioning

confidence: 99%

“…We identified five forecasts of geomorphological dynamics. Cenci et al [163] and San et al [164] have forecast shoreline dynamics in the Mediterranean by calculating trends of shoreline advancement and retreat on transects perpendicular to the coast using high resolution optical satellite imagery. Deng et al [165] and Ma et al [166] employed SAR data to simulate future land subsidence in Beijing and Shanghai, respectively.…”

Section: Lithospherementioning

confidence: 99%

“…In order to accurately model seasonality, however, the observation density in turn must be able to represent intra-annual variances. As a consequence, in the ST category, lead times of more than 10 years have only been achieved using conventional linear trend interpolation [119,164,178] which does not reflect seasonal variances. Those have been be modeled only for short lead times using Box-Jenkins forecasting approaches.…”

Section: Temporal Scopementioning

confidence: 99%

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Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

Koehler

Kuenzer

2020

Remote Sensing

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Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing.

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Section: Categorization Of Forecasting Methodsmentioning

confidence: 99%

Section: Lithospherementioning

confidence: 99%

Section: Temporal Scopementioning

confidence: 99%

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Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

Koehler

Kuenzer

2020

Remote Sensing

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“…Orman and Kaplan [23] reported the sulfur content in the soil and highlighted the tomato monoculture in greenhouses in Kumluca and Finike districts. Also, [24] studied the irrigation waters used in greenhouses in Kumluca and Finike districts; [25] presented the phosphorus distribution and bioavailability in marine surface sediments from differ-Figure 1 Location map of the study area modified from [22] and sample locations ent coasts, one of which is Finike. The only study on heavy metal distribution in Finike was carried out in [26], and the focus of this study was the heavy metal distribution in backshore sediments.…”

Section: Introductionmentioning

confidence: 99%

Data analysis of heavy metal content in riverwater: multivariate statistical analysis and inequality expressions

Leventeli

Yalçın

2021

J Inequal Appl

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The purpose of this paper is to use multivariate statistical methods with asymmetric distributions approach, chemical analysis, and inductively coupled plasma–mass spectrometry (ICP-MS) device. We investigate data of heavy metal content from Akcay Riverwater to the Mediterranean involving Finike sea coast at Turkey. We determine the chemical content, origin of heavy metals of the surface water in Akcay River, which flows into the Mediterranean realted to the above-mentioned region by multivariate statistical analysis, pollution indices, and density maps involving numerical comments by numbers. With the help of special numbers represented by special chemical components and simmetric statistical methods given above, in this paper, we obtain many new relations and results. Furhermore, we give some comments, observations, and remarks about the results of this paper. These results have a high potential to be used not only in engineering fields and health sciences, but also in applied mathematics, statistics, and other fields.

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“…San et al 18 and Aedla et al 19 used linear regression methods to predict future shoreline erosion. Basically, the method was applied into satellite images including millions of pixels.…”

mentioning

confidence: 99%

Shoreline Erosion Assessment Modelling for Sohar Region: Measurements, Analysis, and Scenario

Abushandi

Abualkishik

2020

Sci Rep

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the extended coastlines of oman have been forced to change in the last few decades because of urbanization development or by natural disasters. Recently, Oman has suffered from a couple of tornados and cyclones, e.g. Cyclone Gonu on June 1, 2007, making the changes even much more dynamic. in order to protect the coastal regions infrastructure, an accurate estimation of shoreline erosion is required. this research paper presents an assessment of shoreline erosion magnitudes using field measurements coupled with Multiple Linear Regressions Models (MLR) to predict future changes. inverse Distance Weighing and Kriging interpolation methods have been applied in order to visualize shoreline variations from gathered data prospective. The field measurements for the shoreline were taken at 19 different points, the space between the points in a range of 500-700 m approximately. The first field measurements were taken on 19 th 20 th 21 st of June, 2016 while the second field measurements were taken on 14 th 15 th 16 th of November 2016. Pearson correlation shows a strong relationship between the first and the second field trips with an average of 0.83. This significant relationship ensures the applicability of MLRs to project future changes on the shorelines. The results of the MLRs showed severe negative volumetric shoreline erosion with an average of 5.2 m/year with some exceptions at the catchment outlets.

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An approach for prediction of shoreline with spatial uncertainty mapping (SLiP-SUM)

Cited by 10 publications

References 26 publications

Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

Data analysis of heavy metal content in riverwater: multivariate statistical analysis and inequality expressions

Shoreline Erosion Assessment Modelling for Sohar Region: Measurements, Analysis, and Scenario

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