scholarly journals Application of Hybrid Prediction Methods in Spatial Assessment of Inland Excess Water Hazard

2020 ◽  
Vol 9 (4) ◽  
pp. 268 ◽  
Author(s):  
Annamária Laborczi ◽  
Csaba Bozán ◽  
János Körösparti ◽  
Gábor Szatmári ◽  
Balázs Kajári ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Natural Hazard ◽  
Reference Data ◽  
Auxiliary Information ◽  
Spatial Prediction ◽  
Spatial Modelling ◽  
Excess Water ◽  
Water Hazard ◽  
Water Inundation ◽  
Two Hybrid

Inland excess water is temporary water inundation that occurs in flat-lands due to both precipitation and groundwater emerging on the surface as substantial sources. Inland excess water is an interrelated natural and human induced land degradation phenomenon, which causes several problems in the flat-land regions of Hungary covering nearly half of the country. Identification of areas with high risk requires spatial modelling, that is mapping of the specific natural hazard. Various external environmental factors determine the behavior of the occurrence, frequency of inland excess water. Spatial auxiliary information representing inland excess water forming environmental factors were taken into account to support the spatial inference of the locally experienced inland excess water frequency observations. Two hybrid spatial prediction approaches were tested to construct reliable maps, namely Regression Kriging (RK) and Random Forest with Ordinary Kriging (RFK) using spatially exhaustive auxiliary data on soil, geology, topography, land use, and climate. Comparing the results of the two approaches, we did not find significant differences in their accuracy. Although both methods are appropriate for predicting inland excess water hazard, we suggest the usage of RFK, since (i) it is more suitable for revealing non-linear and more complex relations than RK, (ii) it requires less presupposition on and preprocessing of the applied data, (iii) and keeps the range of the reference data, while RK tends more heavily to smooth the estimations, while (iv) it provides a variable rank, providing explicit information on the importance of the used predictors.

Spatial assessment of inland excess water hazard using combined machine learning and geostatistical methods

2020 ◽  
Author(s):  
Annamária Laborczi ◽  
Csaba Bozán ◽  
Gábor Szatmári ◽  
János Körösparti ◽  
László Pásztor
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Environmental Factors ◽  
Ordinary Kriging ◽  
Auxiliary Information ◽  
Spatial Prediction ◽  
Spatial Modelling ◽  
Regression Kriging ◽  
Excess Water ◽  
Water Hazard

<p>Inland excess water (IEW), considered to be a typical Carpathian Basin land degradation problem, is an interrelated natural and human induced phenomenon, which causes several problems in the flat-land regions of Hungary covering nearly half of the country. The term ‘inland excess water’ refers to the occurrence of inundations outside the flood levee that originate from sources differing from flood overflow, it is surplus surface water forming due to the lack of runoff, insufficient absorption capability of soil or the upwelling of groundwater. There is a multiplicity of definitions, which indicate the complexity of processes that govern this phenomenon. Most of the definitions have a common part, namely, that inland excess water is temporary water inundation that occurs in flat-lands due to both precipitation and groundwater emerging on the surface as substantial sources.<br>Identification of areas with high risk requires spatial modelling, that is mapping of the specific natural hazard. Various external environmental factors determine the behaviour of the occurrence, frequency of IEW. Spatial auxiliary information representing IEW forming environmental factors were taken into account to support the spatial inference of the locally experienced IEW frequency values. Two hybrid spatial prediction approaches, which combine machine learning and geostatistics, were tested to construct reliable maps, namely regression kriging (RK) and Random Forest with Ordinary Kriging (RFK) using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. Both methods divides the spatial inference into two parts. <br>In Regression Kriging the target variable is modelled at first by multiple linear regression (MLR) of the environmental co-variables. Then ordinary kriging is applied on the difference between the reference and the modelled values (residuals). The prediction result map comes from the sum of the MLR model and the interpolated residuals. Random Forest combined with Kriging is a relatively new method applied in digital environmental mapping. In RFK, the deterministic component of spatial variation is modelled by random forest (RF).  RF algorithm builds lots of regression trees and the final model relies on averaging the result of the trees, which are grown independently from each other. In RFK the stochastic part of variation is modelled by kriging using the derived residuals. The final map is the sum of the two component predictions.<br>Comparing the results of the two approaches, we did not find significant differences in their accuracy in our pilot. However, both methods are appropriate for predicting inland excess water hazard, RFK is suitable for revealing non-linear and more complex relations than RK. Therefore, we suggest the usage of RFK in further predictions and investigations.</p><p>Acknowledgement: Our work was supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).</p>

Integrated spatial assessment of inland excess water hazard on the Great Hungarian Plain

2018 ◽  
Vol 29 (12) ◽  
pp. 4373-4386 ◽  
Author(s):  
Csaba Bozán ◽  
Katalin Takács ◽  
János Körösparti ◽  
Annamária Laborczi ◽  
Norbert Túri ◽  
...  
Keyword(s):  
Spatial Assessment ◽  
Great Hungarian Plain ◽  
Excess Water ◽  
Water Hazard ◽  
Hungarian Plain

scholarly journals Spatial Modelling to Inform Public Health Based on Health Surveys: Impact of Unsampled Areas at Lower Geographical Scale

2020 ◽  
Vol 17 (3) ◽  
pp. 786
Author(s):  
Kevin Watjou ◽  
Christel Faes ◽  
Yannick Vandendijck
Keyword(s):  
Public Health ◽  
Health Surveys ◽  
Social Impact ◽  
Impact Analysis ◽  
School Attendance ◽  
Auxiliary Information ◽  
Spatial Modelling ◽  
Population Characteristics ◽  
Demographic And Health Surveys ◽  
Governmental Organizations

Small area estimation is an important tool to provide area-specific estimates of population characteristics for governmental organizations in the context of education, public health and care. However, many demographic and health surveys are unrepresentative at a small geographical level, as often areas at a lower level are not included in the sample due to financial or logistical reasons. In this paper, we investigated (1) the effect of these unsampled areas on a variety of design-based and hierarchical model-based estimates and (2) the benefits of using auxiliary information in the estimation process by means of an extensive simulation study. The results showed the benefits of hierarchical spatial smoothing models towards obtaining more reliable estimates for areas at the lowest geographical level in case a spatial trend is present in the data. Furthermore, the importance of auxiliary information was highlighted, especially for geographical areas that were not included in the sample. Methods are illustrated on the 2008 Mozambique Poverty and Social Impact Analysis survey, with interest in the district-specific prevalence of school attendance.

scholarly journals A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zulfiqar Ali ◽  
Ijaz Hussain ◽  
Muhammad Faisal ◽  
Ibrahim M. Almanjahie ◽  
Muhammad Ismail ◽  
...  
Keyword(s):  
Markov Chain ◽  
Environmental Factors ◽  
Ordinal Data ◽  
Natural Hazard ◽  
Transient Behavior ◽  
Weighting Scheme ◽  
Temperature Index ◽  
Processing Techniques ◽  
Efficient Sequence ◽  
Drought Classes

Drought is a complex stochastic natural hazard caused by prolonged shortage of rainfall. Several environmental factors are involved in determining drought classes at the specific monitoring station. Therefore, efficient sequence processing techniques are required to explore and predict the periodic information about the various episodes of drought classes. In this study, we proposed a new weighting scheme to predict the probability of various drought classes under Weighted Markov Chain (WMC) model. We provide a standardized scheme of weights for ordinal sequences of drought classifications by normalizing squared weighted Cohen Kappa. Illustrations of the proposed scheme are given by including temporal ordinal data on drought classes determined by the standardized precipitation temperature index (SPTI). Experimental results show that the proposed weighting scheme for WMC model is sufficiently flexible to address actual changes in drought classifications by restructuring the transient behavior of a Markov chain. In summary, this paper proposes a new weighting scheme to improve the accuracy of the WMC, specifically in the field of hydrology.

scholarly journals Improving Voting Feature Intervals for Spatial Prediction of Landslides

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Binh Thai Pham ◽  
Tran Van Phong ◽  
Mohammadtaghi Avand ◽  
Nadhir Al-Ansari ◽  
Sushant K. Singh ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Model Performance ◽  
Spatial Prediction ◽  
Susceptibility Assessment ◽  
Improve Performance ◽  
Landslide Susceptibility Assessment ◽  
Ensemble Techniques ◽  
Susceptibility Maps ◽  
Quantitative Validation ◽  
Two Hybrid

In this study, the main aim is to improve performance of the voting feature intervals (VFIs), which is one of the most effective machine learning models, using two robust ensemble techniques, namely, AdaBoost and MultiBoost for landslide susceptibility assessment and prediction. For this, two hybrid models, namely, AdaBoost-based Voting Feature Intervals (ABVFIs) and MultiBoost-based Voting Feature Intervals (MBVFIs) were developed and validated using landslide data collected from one of the landslide affected districts of Vietnam, namely, Muong Lay. Quantitative validation methods including area under the ROC curve (AUC) were used to evaluate model performance. The results indicated that both the newly developed ensemble models ABVFI (AUC = 0.859) and MBVFI (AUC = 0.839) outperformed the single VFI (AUC = 0.824) model. Thus, ensemble framework-based VFI algorithms can be used for the accurate spatial prediction of landslides, which can also be applied in other landslide prone regions of the world. Landslide susceptibility maps developed by ensemble VFI models can be used for better landslide prevention and risk management of the area.

Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information

Geoderma ◽  
2012 ◽  
Vol 171-172 ◽  
pp. 35-43 ◽  
Author(s):  
Shiwen Zhang ◽  
Yuanfang Huang ◽  
Chongyang Shen ◽  
Huichun Ye ◽  
Yichun Du
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Auxiliary Information ◽  
Spatial Prediction ◽  
Categorical Variables ◽  
Terrain Indices

Soil Organic Carbon Prediction at National Scale (Germany)

2020 ◽  
Author(s):  
Ali Sakhaee ◽  
Anika Gebauer ◽  
Mareike Ließ ◽  
Axel Don
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Agricultural Soils ◽  
Model Performance ◽  
Spatial Prediction ◽  
Spatial Modelling ◽  
Agricultural Landscapes ◽  
Boosted Regression Trees ◽  
Environmental Drivers ◽  
National Scale

<p>Soil Organic Carbon (SOC) plays a crucial role in agricultural ecosystems. However, its abundance is spatially variable at different scales. In recent years, machine learning (ML) algorithms have become an important tool in the spatial prediction of SOC at regional to continental scales. Particularly in agricultural landscapes, the prediction of SOC is a challenging task.</p><p>In this study, our aim is to evaluate the capability of two ML algorithms (Random Forest and Boosted Regression Trees) for topsoil (0 to 30 cm) SOC prediction in soils under agricultural use at national scale for Germany. In order to build the models, 50 environmental covariates representing topography, climate factors, land use as well as soil properties were selected. The SOC data we used was from the German Agricultural Soil inventory (2947 sampling points). A nested 5-fold cross-validation was used for model tuning and evaluation. Hyperparameter tuning for both ML algorithms was done by differential evolution optimization. </p><p>This approach allows exploring an extensive set of field data in combination with state of the art pedometric tools. With a strict validation scheme, the geospatial-model performance was assessed. Current results indicate that the spatial SOC variation is to a minor extent predictable with the considered covariate data (<30% explained variance). This may partly be explained by a non-steady state of SOC content in agricultural soils with environmental drivers. We discuss the challenges of geo-spatial modelling and the value of ML algorithms in pedometrics.</p>

scholarly journals Spatial modelling of malaria cases associated with environmental factors in South Sumatra, Indonesia

2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Hamzah Hasyim ◽  
Afi Nursafingi ◽  
Ubydul Haque ◽  
Doreen Montag ◽  
David A. Groneberg ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Spatial Modelling

scholarly journals Assessing inland excess water risk in Kanjiza (Serbia)

Geografie ◽  
2018 ◽  
Vol 123 (2) ◽  
pp. 141-158 ◽  
Author(s):  
Imre Nađ ◽  
Vladimir Marković ◽  
Marko Pavlović ◽  
Uglješa Stankov ◽  
Gordana Vuksanović
Keyword(s):  
Satellite Images ◽  
Risk Level ◽  
Risk Map ◽  
Design Strategies ◽  
Negative Effects ◽  
Local Authorities ◽  
Excess Water ◽  
Gis Tools ◽  
Water Hazard ◽  
Very High

Inland excess waters cause numerous considerable problems in economy, society and environment on a low lying parts of Serbia. The SEERISK methodology has been used for assessing the risk of inland excess water in Kanjiža municipality in Serbia. By applying the GIS tools, inland excess areas were extracted and categorization of different hazards level was done for following cover types: vegetable, orchard, crop, vineyard, grassland and forest. Analysing the satellite images for the selected period (March 2011, August 2012, April 2013 and June 2013), results show that four occurrences of inland excess water were recorded at 0.07% of territory, three occurrences at 0.53% of territory, two occurrences at 3.86%, one occurrence at 9.26% and there were no occurrences at 86.28% of territory. As the final result, the risk map shows four inland excess water risk level zones ranked from “Low”, “Medium”, “High” to “Very high” which can be used by the local authorities in order to design strategies for reducing negative effects from inland excess water hazard.

scholarly journals Voxel-wise and spatial modelling of binary lesion masks: A review and comparison of methods

2021 ◽  
Author(s):  
Petya Kindalova ◽  
Ioannis Kosmidis ◽  
Thomas E. Nichols
Keyword(s):  
Maximum Likelihood ◽  
Spatial Dependence ◽  
Reference Data ◽  
Spatial Modelling ◽  
Maximum Likelihood Estimates ◽  
Data Sets ◽  
Computationally Efficient ◽  
Data Set ◽  
Lesion Mapping ◽  
Modelling Approach

AbstractObjectivesWhite matter lesions are a very common finding on MRI in older adults and their presence increases the risk of stroke and dementia. Accurate and computationally efficient modelling methods are necessary to map the association of lesion incidence with risk factors, such as hypertension. However, there is no consensus in the brain mapping literature whether a voxel-wise modelling approach is better for binary lesion data than a more computationally intensive spatial modelling approach that accounts for voxel dependence.MethodsWe review three regression approaches for modelling binary lesion masks including massunivariate probit regression modelling with either maximum likelihood estimates, or mean bias-reduced estimates, and spatial Bayesian modelling, where the regression coefficients have a conditional autoregressive model prior to account for local spatial dependence. We design a novel simulation framework of artificial lesion maps to compare the three alternative lesion mapping methods. The age effect on lesion probability estimated from a reference data set (13,680 individuals from the UK Biobank) is used to simulate a realistic voxel-wise distribution of lesions across age. To mimic the real features of lesion masks, we suggest matching brain lesion summaries (total lesion volume, average lesion size and lesion count) across the reference data set and the simulated data sets. Thus, we allow for a fair comparison between the modelling approaches, under a realistic simulation setting.ResultsOur findings suggest that bias-reduced estimates for voxel-wise binary-response generalized linear models (GLMs) overcome the drawbacks of infinite and biased maximum likelihood estimates and scale well for large data sets because voxel-wise estimation can be performed in parallel across voxels. Contrary to the assumption of spatial dependence being key in lesion mapping, our results show that voxel-wise bias-reduction and spatial modelling result in largely similar estimates.ConclusionBias-reduced estimates for voxel-wise GLMs are not only accurate but also computationally efficient, which will become increasingly important as more biobank-scale neuroimaging data sets become available.

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