Analytical techniques for mapping multi-hazard with geo-environmental modeling approaches and UAV images.

The quantitative spatial analysis is a strong tool for the study of natural hazards and their interactions. Over the last decades, a range of techniques have been exceedingly used in spatial analysis, especially applying GIS and R software. In the present paper, the multi-hazard susceptibility maps compared in 2020 and 2021 using an array of data mining techniques, GIS tools, and Unmanned aerial vehicles. The produced maps imply the most effective morphometric parameters on collapsed pipes, gully heads, and landslides using the linear regression model. The multi-hazard maps prepared using seven classifiers of Boosted regression tree (BRT), Flexible discriminant analysis (FDA), Multivariate adaptive regression spline (MARS), Mixture discriminant analysis (MDA), Random forest (RF), Generalized linear model (GLM), and Support vector machine (SVM). The results of each model revealed that the greatest percentage of the study region was low susceptible to collapsed pipes, landslides, and gully heads, respectively. The results of the multi-hazard models represented that 52.22% and 48.18% of the study region were not susceptible to any hazards in 2020 and 2021, while 6.19% (2020) and 7.39% (2021) of the region were at the risk of all compound events. The validation results indicate the area under the receiver operating characteristic curve of all applied models was more than 0.70 for the landform susceptibility maps in 2020 and 2021. It was found where multiple events co-exist, what their potential interrelated effects are or how they interact jointly. It is the direction to take in the future to determine the combined effect of multi-hazards so that policymakers can have a better attitude toward sustainable management of environmental landscapes and support socio-economic development.

Investigating geometrical characteristics of collapsed pipes and the changing role of driving factors.

Determination of the amount (i.e., area and volume) of soil losses due to erosional landforms, especially collapsed pipes, plays a considerable role in different decision-making approaches. Further, mapping the spatial distribution and predicting the volumetric and areal losses of collapsed pipes (CPs) are essential for supporting ecosystem health. The study was conducted in relation to the area and volume of CPs and their related covariables. It focused on the estimation of soil losses due to collapsed pipes using unmanned aerial vehicle (UAV) images as well as field covariates at the Chatal Watershed, Golestan Province, Iran. A total of 481 soil samples were collected from homogeneous units with an area of approximately 1,410 ha. The potential relationship between the area/volume of collapsed pipes and land use, several topographic attributes (i.e., altitude, slope, and aspect), and soil properties, including soil stability, soil organic matter, clay, silt, and sand contents were analyzed using five distance-based methods (i.e., kernel density (KD), average nearest neighbor (ANN), spatial autocorrelation, hotspot analysis (HSA), and ordinary least square (OLS) analysis. The average nearest neighbor (Ratio = 0.12, Z score = -20.30, p-value < 0.05) and Moran space solidarity (Moran index = 0.258, Z score = 5.50, p-value < 0.05) showed the cluster distribution of area and volume of CPs. Hot spots and cold spots in the southwestern part of the study area were identified using KD and HSA. The relationship between existing independent and dependent variables (area of CPs) using regression analysis of OLS showed that slope and aggregate stability (>2.5 standard deviation) had the highest positive relationship with the dependent variable. Regarding the volume of CPs, land use (especially agricultural lands) had the strongest relationship with the dependent variable. Thus, geometrical characteristics of collapsed pipes can be applied as a quantitative indicator for the identification of hotspot zones (hazardous areas), land use planning, and erosion hazard mitigation. However, more studies are required to measure geometrical characteristics of soil landforms.

Evaluation of factors affecting gully headcut location using summary statistics and the maximum entropy model: Golestan Province, NE Iran.

Gully erosion is an important soil degradation process, which under climate changes is projected to increase. Therefore, better understating of factors controlling gully erosion and prediction of gully headcuts' (GHs) location is still highly relevant. This study aimed to examine the spatial distribution of GHs and to assess the importance of pedological (i.e. aggregate stability, organic matter, bulk density, silt, clay, and sand content) and topographical factors (i.e. altitude, slope length, gradient, and aspect) using summary statistics and the maximum entropy (MaxEnt) model. The study was conducted in the loess-covered region of NE Iran. The highly precise data of 287 GHs locations were obtained by extensive fieldwork and the interpretation of UAV images. The spatial distribution of GHs was evaluated by univariate pair correlation function and O-ring statistics. The spatial effect of GHs density controlling factors was assessed by the cumulative density correlation function Cm,K(r). Variable importance was analyzed using the MaxEnt model, which was also for the susceptibility modelling of GHs. The results of univariate tests showed the aggregated distribution of GHs. The Cm,K(r) analyses indicated that the areas characterized by higher values of bulk density, aggregate stability, and organic matter content have lower GHs density, whereas the areas with high silt content and higher slope gradient have higher GHs density. According to the MaxEnt, there is no one single factor responsible for GHs location, but rather the combination of topographical and pedological factors with the predominance of slope gradient (0.86) and silt content (0.57). The MaxEnt modelling of GHs susceptibility has revealed that the best accuracy (0.958) is given when all pedological and topographical factors are used in the model. The susceptibility maps prepared in the study can be used for soil conversation and land use planning and, consequently, for sustainable development in the region.

How can statistical and artificial intelligence approaches predict piping erosion susceptibility?

It is of fundamental importance to model the relationship between geo-environmental factors and piping erosion because of the environmental degradation attributed to soil loss. Methods that identify areas prone to piping erosion at the regional scale are limited. The main objective of this research is to develop a novel modeling approach by using three machine learning algorithms-mixture discriminant analysis (MDA), flexible discriminant analysis (FDA), and support vector machine (SVM) in addition to an unmanned aerial vehicle (UAV) images to map susceptibility to piping erosion in the loess-covered hilly region of Golestan Province, Northeast Iran. In this research, we have used 22 geo-environmental indices/factors and 345 identified pipes as predictors and dependent variables. The piping susceptibility maps were assessed by the area under the ROC curve (AUC). Validation of the results showed that the AUC for the three mentioned algorithms varied from 90.32% to 92.45%. We concluded that the proposed approach could efficiently produce a piping susceptibility map.

Combined effects of polyacrylamide and nanomagnetite amendment on soil and water quality, Khorasan Razavi, Iran.

Nanotechnology is increasingly being used to remediate polluted soil and water. However, few studies are available assessing the potential of nanoparticles to bind surface particles, decrease erosion, and minimize the loading of water pollutants from agricultural surface discharge. To investigate this potential, we treated in situ field plots with two practical surface application levels of anionic polyacrylamide (PAM only) with and without nanomagnetite (PAM-NM), examined soil physical properties, and evaluated the impact of this amendment on contaminant sorption and soil erosion control. Polyacrylamide and PAM-NM treatments resulted in 32.2 and 151.9 fold reductions in Mn2+, 1.8 and 2.7 fold for PO43--P, and 2.3 and 1.6 fold for NH4+-N, respectively, compared to the control. Thus, we found that the combination of PAM and NM, had an important inhibitory effect on NH4+-N and PO43--P transport from soil-pollutants which can contribute substantially to the eutrophication of surface water bodies. Additionally, since the treatment, especially at a high concentration of NM, was effective at reducing Mn2+concentrations in the runoff water, the combination of PAM and NM may be important for mitigating potential risks associated with Mn2+ toxicity. Average sediment contents in the runoff monitored during the rainfall simulation were reduced by 3.6 and 4.2 fold for the low and high concentration PAM-NM treatments when compared to a control. This treatment was only slightly less effective than the PAM-only applications (4.9 and 5.9 fold, respectively). We report similar findings for turbidity of the runoff (2.6-3.3 fold for PAM only and 1.8-2.3 fold for PAM-NM) which was caused by the effects of both PAM and NM on the binding of surface particles corresponding to an increase in aggregate size and stability. Findings from this field-based study show that PAM-modified NM adsorbents can be used to both inhibit erosion and control contaminant transport.