Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors

Al-Juaidi, Ahmed E. M.; Nassar, Ayman; Aljuaidi, Omar

doi:10.1007/s12517-018-4095-0

Cited by 153 publications

(38 citation statements)

References 34 publications

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“…Several data-driven models have been developed and used for flood mapping, including bivariate models of frequency ratio [16,17], Shannon entropy [18], weight of evidence (WOE) [11], and the evidential belief function (EBF) [16]. In addition, a variety of multivariate methods have been used in flood hazard studies, notably logistic regression [19,20] and multicriteria decision-making (MCDM) methods such as analytic hierarchy process (AHP) [21][22][23] analytic network process (ANP) [24], vlse kriterijuska optamizacija I komoromisno resenje (VIKOR), and a technique for order preference by similarity to ideal solution (TOPSIS) [25]. Unfortunately, many of these models have performance limitations in that they do not incorporate nonflood locations and generally consider only sum weights or class weights rather than weights for specific layers [26].…”

Section: Introductionmentioning

confidence: 99%

Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier

et al. 2020

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Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas.

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Section: Introductionmentioning

confidence: 99%

Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier

et al. 2020

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“…Once 300 inventory points were acquired, 300 nonfire points were compiled and the data was divided by random selection from the total inventory points according to the standard 70% training, 30% testing proportion [57][58][59].…”

Section: Datamentioning

confidence: 99%

Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping

et al. 2021

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The survival of humanity is dependent on the survival of forests and the ecosystems they support, yet annually wildfires destroy millions of hectares of global forestry. Wildfires take place under specific conditions and in certain regions, which can be studied through appropriate techniques. A variety of statistical modeling methods have been assessed by researchers; however, ensemble modeling of wildfire susceptibility has not been undertaken. We hypothesize that ensemble modeling of wildfire susceptibility is better than a single modeling technique. This study models the occurrence of wildfire in the Brisbane Catchment of Australia, which is an annual event, using the index of entropy (IoE), evidential belief function (EBF), and logistic regression (LR) ensemble techniques. As a secondary goal of this research, the spatial distribution of the wildfire risk from different aspects such as urbanization and ecosystem was evaluated. The highest accuracy (88.51%) was achieved using the ensemble EBF and LR model. The outcomes of this study may be helpful to particular groups such as planners to avoid susceptible and risky regions in their planning; model builders to replace the traditional individual methods with ensemble algorithms; and geospatial users to enhance their knowledge of geographic information system (GIS) applications.

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“…Spatial data from the digital terrain model (DTM) [67] and digital surface model (DSM) were aggregated into multiple spatial scales by employing a simple averaging method; then, h c was calculated using the expression: h c = DSM-DTM. For example, in the case of a 7.2-m grid, the average values of DSM and DTM, DSM (7.2) , and DTM (7.2) , respectively, were computed inside the grid window, then the height of the canopy was computed as: h c (7.2) = DSM (7.2) − DTM (7.2) .…”

Section: Canopy Height (H C )mentioning

confidence: 99%

Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards

et al. 2020

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Evapotranspiration (ET) is a key variable for hydrology and irrigation water management, with significant importance in drought-stricken regions of the western US. This is particularly true for California, which grows much of the high-value perennial crops in the US. The advent of small Unmanned Aerial System (sUAS) with sensor technology similar to satellite platforms allows for the estimation of high-resolution ET at plant spacing scale for individual fields. However, while multiple efforts have been made to estimate ET from sUAS products, the sensitivity of ET models to different model grid size/resolution in complex canopies, such as vineyards, is still unknown. The variability of row spacing, canopy structure, and distance between fields makes this information necessary because additional complexity processing individual fields. Therefore, processing the entire image at a fixed resolution that is potentially larger than the plant-row separation is more efficient. From a computational perspective, there would be an advantage to running models at much coarser resolutions than the very fine native pixel size from sUAS imagery for operational applications. In this study, the Two-Source Energy Balance with a dual temperature (TSEB2T) model, which uses remotely sensed soil/substrate and canopy temperature from sUAS imagery, was used to estimate ET and identify the impact of spatial domain scale under different vine phenological conditions. The analysis relies upon high-resolution imagery collected during multiple years and times by the Utah State University AggieAirTM sUAS program over a commercial vineyard located near Lodi, California. This project is part of the USDA-Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Original spectral and thermal imagery data from sUAS were at 10 cm and 60 cm per pixel, respectively, and multiple spatial domain scales (3.6, 7.2, 14.4, and 30 m) were evaluated and compared against eddy covariance (EC) measurements. Results indicated that the TSEB2T model is only slightly affected in the estimation of the net radiation (Rn) and the soil heat flux (G) at different spatial resolutions, while the sensible and latent heat fluxes (H and LE, respectively) are significantly affected by coarse grid sizes. The results indicated overestimation of H and underestimation of LE values, particularly at Landsat scale (30 m). This refers to the non-linear relationship between the land surface temperature (LST) and the normalized difference vegetation index (NDVI) at coarse model resolution. Another predominant reason for LE reduction in TSEB2T was the decrease in the aerodynamic resistance (Ra), which is a function of the friction velocity ( u * ) that varies with mean canopy height and roughness length. While a small increase in grid size can be implemented, this increase should be limited to less than twice the smallest row spacing present in the sUAS imagery. The results also indicated that the mean LE at field scale is reduced by 10% to 20% at coarser resolutions, while the with-in field variability in LE values decreased significantly at the larger grid sizes and ranged between approximately 15% and 45%. This implies that, while the field-scale values of LE are fairly reliable at larger grid sizes, the with-in field variability limits its use for precision agriculture applications.

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Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors

Cited by 153 publications

References 34 publications

Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier

Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier

Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping

Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards

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