Comparing physiographic maps with different categorisations

Zawadzka, Joanna; Mayr, Thomas; Bellamy, P.; Corstanje, Ronald

doi:10.1016/j.geomorph.2014.12.006

Cited by 11 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The "DescTools" package (Signorell, 2016) written in R programming language was employed in this study for estimating the nominal and categorical association between observed and simulated maps. (Cramér, 1999) statistic is a chi-square-testbased measure which is used in assessing spatial agreement between observations and model simulations (Zawadzka et al, 2015). Its value ranges between 0 and 1 and value closer to 1 refers to a better agreement between the simulated and observed maps of the climate variable.…”

Section: Goodman-kruskal's Lambdamentioning

confidence: 99%

Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics

Ahmed

Sachindra

Shahid

et al. 2019

Hydrol. Earth Syst. Sci.

182

114

View full text Add to dashboard Cite

Abstract. The climate modelling community has trialled a large number of metrics for evaluating the temporal performance of general circulation models (GCMs), while very little attention has been given to the assessment of their spatial performance, which is equally important. This study evaluated the performance of 36 Coupled Model Intercomparison Project 5 (CMIP5) GCMs in relation to their skills in simulating mean annual, monsoon, winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum temperature over Pakistan using state-of-the-art spatial metrics, SPAtial EFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V, Mapcurves, and Kling–Gupta efficiency, for the period 1961–2005. The multi-model ensemble (MME) precipitation and maximum and minimum temperature data were generated through the intelligent merging of simulated precipitation and maximum and minimum temperature of selected GCMs employing random forest (RF) regression and simple mean (SM) techniques. The results indicated some differences in the ranks of GCMs for different spatial metrics. The overall ranks indicated NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 as the best GCMs in simulating the spatial patterns of mean annual, monsoon, winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum temperature over Pakistan. MME precipitation and maximum and minimum temperature generated based on the best-performing GCMs showed more similarities with observed precipitation and maximum and minimum temperature compared to precipitation and maximum and minimum temperature simulated by individual GCMs. The MMEs developed using RF displayed better performance than the MMEs based on SM. Multiple spatial metrics have been used for the first time for selecting GCMs based on their capability to mimic the spatial patterns of annual and seasonal precipitation and maximum and minimum temperature. The approach proposed in the present study can be extended to any number of GCMs and climate variables and applicable to any region for the suitable selection of an ensemble of GCMs to reduce uncertainties in climate projections.

show abstract

Section: Goodman-kruskal's Lambdamentioning

confidence: 99%

Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics

Ahmed

Sachindra

Shahid

et al. 2019

Hydrol. Earth Syst. Sci.

182

114

View full text Add to dashboard Cite

show abstract

“…Cramer's V and Contingency tests are chi-square-based measures, while Joint Information Uncertainty is based on Joint Entropy measure [77]. Cramer's V is the most popular of the Chi-square-based measures of nominal association [78] and was indicated as one of the most suitable measure of association between two categorical maps [79]. In the MLP-MC simulation using LCM, to quantify the association between each LU/LC with the driving variables, a Cramer's V analysis was conducted.…”

Section: Testing the Selected Driving Factorsmentioning

confidence: 99%

“…Cramer's V analysis is a way of calculating correlation in tables that have more than 2 × 2 rows and columns [80]. Cramer's V value ranges from 0 to 1.0 regardless of table size [78]. This makes it possible to use Cramer's V to compare the strength of association between any two cross classification tables [81].…”

Section: Testing the Selected Driving Factorsmentioning

confidence: 99%

Urban Growth Simulation of Atakum (Samsun, Turkey) Using Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models

Öztürk

2015

Remote Sensing

148

View full text Add to dashboard Cite

Abstract:In this study, urban growth of the Atakum District in Samsun, Turkey, was simulated by Cellular Automata-Markov Chain (CA-MC) and Multi-layer Perceptron-Markov Chain (MLP-MC) hybrid models in a geographical information system (GIS) environment. Historical land use/land cover (LU/LC) data were extracted from 1989, 2000 and 2013 Landsat TM/ETM+/OLI images. Using the LU/LC data for the years 1989 and 2000, the urban growth for 2013 was simulated using the CA-MC and MLP-MC models. The simulation results were compared with the 2013 LU/LC data to assess the validity of the simulation. The MLP-MC method provided the best results according to the validation based on the kappa index of agreement. Based on this result, the urban growth for the year 2025 was simulated using MLP-MC. The simulation estimated an urban growth rate of 35.2% between 2013 and 2025, an increase in the area of artificial surfaces from 1681.9 ha to 2274.3 ha and the destruction of 511.7 ha of agricultural land and 4.4 ha of forest. The results of this study demonstrate that the urban growth models provide a better understanding of the current patterns and temporal dynamics and can predict future changes according to past and current dynamics. The results also show that simulations are most accurate when using a model that best conforms to the changes in the given study area.

show abstract

“…Cramer's V (Cramér, 1999) statistic is a Chi-square-test-based measure which is used in assessing spatial agreement between observations and model simulations (Zawadzka et al, 2015). Its value ranges between 0 and 1 and can be calculated using…”

Section: Cramer's Vmentioning

confidence: 99%

Selection of multi-model ensemble of GCMs for the simulation of precipitation based on spatial assessment metrics

Ahmed

Sachindra

Shahid

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. The climate modelling community has trialled a large number metrics to evaluate the temporal performance of the Global Circulation Models (GCMs) for the selection of GCMs, while very little attention has been given to spatial performance of GCMs which is equally important. This study evaluated the performance of 20 Coupled Model Intercomparison Project 5 (CMIP5) GCMs pertaining to their skills in simulating mean annual, monsoon and winter precipitation over Pakistan using state-of-the-art spatial metrics; SPAtial EFficiency, Goodman–Kruskal's lambda, Fractions Skill Score, Cramer’s V, Mapcurves, and Kling-Gupta efficiency for the period 1961–2005. The multi-model ensemble (MME) precipitation was generated through intelligent merging of simulated precipitation of selected GCMs employing Random Forest (RF) regression and Simple Mean (SM). The results indicated some differences in the ranks of GCMs for different metrics. The overall ranks indicated NorESM1-M, CESM1-CAM5, GFDL-CM3 and GFDL-ESM2G as the best GCMs in simulating the spatial patterns of mean annual, monsoon and winter precipitation over Pakistan. MME precipitation generated based on the best performing GCMs showed more similarities with observed precipitation compared to precipitation simulated by individual GCMs. The MME developed using RF displayed better performance than the MME-based on SM. Multiple spatial metrics have been used for the first time for selecting GCMs based on their capability to mimic the spatial patterns of annual and seasonal precipitation. The approach suggested in the present study can be extended to any number of GCMs and climate variables and applicable to any region for the suitable selection of an ensemble of GCMs to reduce uncertainties in climate projections.

show abstract

Comparing physiographic maps with different categorisations

Cited by 11 publications

References 23 publications

Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics

Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics

Urban Growth Simulation of Atakum (Samsun, Turkey) Using Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models

Selection of multi-model ensemble of GCMs for the simulation of precipitation based on spatial assessment metrics

Contact Info

Product

Resources

About