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

Abstract

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.

Joint Spatial Modeling of Nutrients and Their Ratio in the Sediments of Lake Balaton (Hungary): A Multivariate Geostatistical Approach

“…Note that predictions refer to the uppermost 10 cm of the sediment layer of the lake. In addition to the spatial predictions, we also quantified the associated prediction uncertainty, which was expressed by the upper and lower limit of the 90% PI that is frequently used in environmental mapping (e.g., [27,29,48]), as presented in Figure 5 (left and right columns). Figure 6 gives a more detailed picture of the Keszthely basin where most of the N observations had to be excluded from spatial modeling and only data on P were available.…”

“…Furthermore, it tries to exploit this spatial interdependence in order to give coherent and even more accurate spatial predictions for the variables of interest [23][24][25][26]. Moreover, multivariate geostatistics is able to model and quantify the uncertainty associated with a spatial prediction, which has become a common requirement in the practice of environmental modeling and mapping [27][28][29][30].…”

Spatial assessment of the inland excess water presence on subsurface drained areas in the Körös Interfluve (Hungary)

“…and changes over time (causing IEW inundation, e.g. hydrometeorology, groundwater) (LABORCZI et al, 2020a;BARTA et al, 2013). BARTA et al (2016) have shown that infrastructural elements and linear facilities (road and railway network, canals, embankments, etc.)…”

“…This hydrological phenomenon practically may occur in all lowland areas with poor runoff and unfavourable water management properties of soils (LABORCZI et al, 2020a). Many approaches have been published to describe IEW which are very diverse: (1) the in situ mapping of IEW ( VAN LEEUWEN et al, 2013), (2) the determination of the extent and probability of IEW hazard (BOZÁN et al, 2018;LABORCZI et al, 2020a;LABORCZI et al, 2020b), and the modeling of its movement phenomena (KONCSOS, 2011) are the most studied topics to researchers. NAĐ et al (2018) adapted the SEERISK methodology to assess the risk of IEW appearance.…”

Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative

“…Digital soil mapping (DSM), which has become a successful sub-discipline of soil science with an active research output [10], aims to provide spatial soil information for a wide range of studies, such as precision agriculture [11,12], hydrology [13][14][15], environmental sciences [16,17], conservation biology [18,19] or spatial planning [20,21]. For this purpose, geostatistical techniques are widely used, which have been complemented by machine learning algorithms in the past decade.…”