Application of Fuzzy Best Worse Multi Criteria Decision Making Method for Flood Prioritization

Flood is one of the major problems of the sad ekbatan watershed, northern of Hamadan province, Iran. This problem imposes high damages to the economic issue. Therefore, prioritization of the study area based on the flooding degree can be considered for identifying hot spot flooded areas for performing soil and water conservation practices. In this study, in order to prioritize sub-watersheds of the case study from viewpoint of flooding degree, five flood-related criteria i.e. entropy of drainage network (En), index of connectivity (IC), stream power index (SPI), curvature (C) and curve number (CN) were considered, then fuzzy based Best Worse Multi Criteria Decision Making (F-BWM) Method was used to assigning weights to used criteria and combination them to achieve flooding degree for each sub-watershed. The results of prioritization of sub-watersheds indicated that the sub-watersheds 14 and 21 are most and least susceptibility areas to flooding correspondingly.

Effects of Glacial Isostatic Adjustment on Surface Topography, Flow Accumulation, Stream Power & Sediment Transport Indexes in the Canadian Prairies

<p>Glacial Isostatic Adjustment (GIA) induced by the melting of the Pleistocene Ice Sheets causes differential land uplift, relative sea level and geoid changes. Thus, GIA in North America may affect water flow-accumulation and the rate of sedimentation and erosion in the South Saskatchewan River Basin (SSRB), but so far this has not been well investigated.</p><p> </p><p>Our aim here is to use surface topography in the SSRB and simple models of surface water flow to compute flow-accumulation, wetness index, stream power index and sediment transport index - the latter two affect the rates of erosion and sedimentation. Since the river basin became virtually ice-free around 8 ka BP, we shall study the effects of GIA induced differential land uplift during the last 8 ka on these indexes.</p><p> </p><p>Using the present-day surface topography ETOPO1 model, we see that the stream power index and sediment transport index in the SSRB may not be high enough to alter the surface topography significantly today and probably during the last 8 ka except for places around the Rocky Mountains. The effect of using 1 and 3 arc minute grid resolution of the ETOPO1 model does not significantly alter the value of these indexes. However, we note that using 1 arc minute grid is much more computationally intensive, so only a smaller area of the SSRB can be included in the computation.</p><p> </p><p>Next, we assume that sedimentation and erosion did not occur in the SSRB during the last 8 ka BP, and the change in surface topography is only due to GIA induced differential uplift. We use land uplift predicted by a large number of GIA models to study the changes in stream power & sediment transport indexes in the last 8 ka BP. Our base GIA model is ICE6G_C(VM5a). Then we investigate the effects of using uplift predicted by other GIA models that can still fit the observed relative sea level (RSL), uplift rate and gravity-rate-of-change data in North America reasonably well. These alternate GIA models have lateral heterogeneity in the mantle and lithosphere included – in particular we test those that give the largest differential uplift in the SSRB. We found that the effect of these other GIA earth models is not large on the stream power & sediment transport indexes. Finally, we investigate the sensitivity of these indexes on the ice models that are consistent with GIA observations. The results of this study will be useful to our understanding of water flow accumulation, sedimentation and erosion in the past, present and future and for water resource management in North America.</p>

The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

Understanding the potential groundwater resource distribution is critical for sustainable groundwater development, conservation, and management strategies. This study analyzes and maps the groundwater potential in Busan Metropolitan City, South Korea, using random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB) methods. Fourteen groundwater conditioning factors were evaluated for their contribution to groundwater potential assessment using an elastic net. Curvature, the stream power index, the distance from drainage, lineament density, and fault density were excluded from the subsequent analysis, while nine other factors were used to create groundwater potential maps (GMPs) using the RF, GBM, and XGB models. The accuracy of the resultant GPMs was tested using receiver operating characteristic curves and the seed cell area index, and the results were compared. The analysis showed that the three models used in this study satisfactorily predicted the spatial distribution of groundwater in the study area. In particular, the XGB model showed the highest prediction accuracy (0.818), followed by the GBM (0.802) and the RF models (0.794). The XGB model, which is the most recently developed technique, was found to best contribute to improving the accuracy of the GPMs. These results contribute to the establishment of a sustainable management plan for groundwater resources in the study area.

GIS-based MCDA for Gully Vulnerability Mapping Using AHP Techniques

This paper explores the potentiality of GIS-based Multi-Criteria Decision Analysis (MCDA) and Analytical Hierarchy Process (AHP) for gully vulnerability mapping. Multilayer information of basin characteristics, such as drainage density, Topographic Wetness Index (TWI), Stream Power Index (SPI), slope aspect and land use land cover (LULC), were used in this study to develop a Gully Vulnerability Index (GVI). A weighted approach was implemented on each criterion relative to their inferred influence on gully vulnerability and validated by determining the Consistency Ratio (CR). Findings show a varying magnitude of gully vulnerability across the study area. The low to medium gully vulnerability class was dominant covering a land area of 6557ha (21.25%), and mostly confined to developed areas. Still, it is noteworthy to observe that the severe gully vulnerability class covers a substantial land area of 5825ha (18.88%), which presents a great risk to infrastructural development and human settlements in the study area. The study has a model predictive capability with accuracy rate of 84.62%. The integration of the MCDA and AHP into GIS workflow is an effective approach critical to minimize the limitations associated with gully occurrence analysis, using a singular basin characteristic. The results obtained in the study will equally be important in determining gully risk zones, circumspect urban development, tracking and proper infrastructure construction plans for long-term gully disaster mitigation.

Can Lumped Characteristics of a Contributing Area Provide Risk Definition of Sediment Flux?

Accelerated soil erosion by water has many offsite impacts on the municipal infrastructure. This paper discusses how to easily detect potential risk points around municipalities by simple spatial analysis using GIS. In the Czech Republic, the WaTEM/SEDEM model is verified and used in large scale studies to assess sediment transports. Instead of computing actual sediment transports in river systems, WaTEM/SEDEM has been innovatively used in high spatial detail to define indices of sediment flux from small contributing areas. Such an approach has allowed for the modeling of sediment fluxes in contributing areas with above 127,484 risk points, covering the entire Czech Republic territory. Risk points are defined as outlets of contributing areas larger than 1 ha, wherein the surface runoff goes into residential areas or vulnerable bodies of water. Sediment flux indices were calibrated by conducting terrain surveys in 4 large watersheds and splitting the risk points into 5 groups defined by the intensity of sediment transport threat. The best sediment flux index resulted from the correlation between the modeled total sediment input in a 100 m buffer zone of the risk point and the field survey data (R2 from 0.57 to 0.91 for the calibration watersheds). Correlation analysis and principal component analysis (PCA) of the modeled indices and their relation to 11 lumped characteristics of the contributing areas were computed (average K-factor; average R-factor; average slope; area of arable land; area of forest; area of grassland; total watershed area; average planar curvature; average profile curvature; specific width; stream power index). The comparison showed that for risk definition the most important is a combination of morphometric characteristics (specific width and stream power index), followed by watershed area, proportion of grassland, soil erodibility, and rain erosivity (described by PC2).

Assessment of shallow seated landslide size and magnitude characteristics: An example from Northeastern Turkey

<p>Landslides occurred in the Northeastern part of Turkey are generally classified to be shallow seated landslides or earthflow type based on Varnes (1984) classification. These landslides are occasionally seen in the weathered  Eocene or Upper Crateceous aged volcanic and volcano-clastic rocks. Although there are considerable studies both directly on these landslides in point of mapping and hazard assessment, there is no any studies concerning size and magnitude characteristics of them. By considering this point, an assessment of size and magnitude characteristics of shallow seated type landslides at an area where is one of most landslide prone area of Turkey was carried out.</p><p>The investigation area is totaly covered by Eocene aged volcano-clastic lithology, and the weathering is widespread due to the climatical conditions in the area. The extend of the area is 140 square kilometers. In the area, 120 landslides were mapped by a multiple image interpretation that is from the years of 2000 to 2019. To do this, Google Earth images were used. In the area, the area (A<sub>L</sub>) of the landslide mapped differs from 53.28 m<sup>2</sup> to 902,809 m<sup>2</sup>. The length and width of these landslide were also determined and these characteristics were taken into account for an assessment of relationship between the size and topographical features such as slope gradient, curvature, topographical wetness index and stream power index. The approximate volumes of these landslides were calculated by considering direct depth observations in the field surveys, and then assessed by different relations proposed by different studies. Magnitude (M) of these landslides were also assessed by taken into account of the area (A<sub>L</sub>) and volume (V<sub>L</sub>) values.</p>

A data-mining approach to investigate El Niño damage in Peru

<p>Compound natural hazards, like El Niño events, which trigger torrential rain, mudslides, riverine and flash floods, cause high damage to society. An improved risk management based on reliable risk assessments are urgently needed. However, knowledge about the complex processes leading to El Niño damage is lacking, and so are loss models.  We explore a large dataset of building damage from the coastal El Niño event 2017 in Peru. We use data-mining techniques to analyse data of damage grades of about 180.000 affected houses together with satellite observations and open geo-information. In a first step, we use unsupervised clustering (t-SNE + OPTICS) to separate regions of different dominant processes. Secondly, we train various supervised classification algorithms and create feature importance rankings per cluster, to identify drivers of observed damage for each of these regions. A comparison of different algorithms provides further insights about the potential and limitations of these methods and datasets. Results indicate that topographic wetness is the most important indicator, as selected by the algorithms, when using the entire dataset. Rainfall sum and maximum from TRMM satellite measurements are identified as damage driver despite the coarse spatial resolution. Also urbanity, based on a focal window around the global urban footprint, appears to play a role for the amount of damage. At least a coarse separation of processes is possible: the slope length and steepness, bare soil index, stream power index, and maximum rainfall are dominating the damage processes in lower mountain ranges and canyons, indicating rapid processes. Damage in upper mountain areas seem more influenced by the rainfall sum, local topographic position, and vegetation cover. In the lowlands, topographic wetness is very dominant, indicating ponding water or riverine floods. As opposed to previous work, this study constructs importance rankings based entirely on real observed damage to buildings. It is therefore a step towards data-driven damage assessments for El Niño events.</p>

Harris Hawks Optimization: A Novel Swarm Intelligence Technique for Spatial Assessment of Landslide Susceptibility

In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors—elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall—is prepared to develop the ANN and HHO–ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO–ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO–ANN = 0.773) the landslide pattern.

Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data

Accurate forest above-ground biomass (AGB) mapping is crucial for sustaining forest management and carbon cycle tracking. The Shuttle Radar Topographic Mission (SRTM) and Sentinel satellite series offer opportunities for forest AGB monitoring. In this study, predictors filtered from 121 variables from Sentinel-1 synthetic aperture radar (SAR), Sentinal-2 multispectral instrument (MSI) and SRTM digital elevation model (DEM) data were composed into four groups and evaluated for their effectiveness in prediction of AGB. Five evaluated algorithms include linear regression such as stepwise regression (SWR) and geographically weighted regression (GWR); machine learning (ML) such as artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that the RF model used predictors from both the Sentinel series and SRTM DEM performed the best, based on the independent validation set. The RF model achieved accuracy with the mean error, mean absolute error, root mean square error, and correlation coefficient in 1.39, 25.48, 61.11 Mg·ha−1 and 0.9769, respectively. Texture characteristics, reflectance, vegetation indices, elevation, stream power index, topographic wetness index and surface roughness were recommended predictors for AGB prediction. Predictor variables were more important than algorithms for improving the accuracy of AGB estimates. The study demonstrated encouraging results in the optimal combination of predictors and algorithms for forest AGB mapping, using openly accessible and fine-resolution data based on RF algorithms.