Flood Susceptibility Mapping Using Random Forest Machine Learning and Generalized Bayesian Linear Model

Today, the phenomenon of flooding is one of the most complex hazardous events that, more than any other natural disaster, causes deaths and finances every year in different parts of the world. Therefore, flood susceptibility mapping is the first step in a flood management program. The purpose of this study was to identify flood susceptible areas using two methods of random forest (RF) and Bayesian generalized linear model (GLMbayesian) machine learning in the Tajan watershed in Mazandaran province, Sari. Past flood distribution maps were prepared to predict future floods. Of the 263 flood locations, 80% (210 flood locations) was used for modeling and 20% (53 flood locations) was used for validation. Based on previous studies and surveying of the study area, 13 conditional factors were selected for flood zoning. The results showed that three factors of elevation (21.55), distance from the river (15.28) and slope (11.18) had the highest impact on flood occurrence in the study area, respectively. The results also showed that the AUC values for RF and GLMbayesian models were 0.91 and 0.847, respectively, indicating the superiority of the RF model and the accuracy of this model in flood susceptibility mapping in the study area. The highest flood susceptibility area in the RF model is in the very low class and the high class in the GLMbayesian model

Analysis of Landscape Composition and Configuration Based on LULC Change Modeling

Land cover changes threaten biodiversity by impacting the natural habitats and require careful and continuous assessment. The standard approach for assessing these changes is land cover modeling. The present study investigated the spatio-temporal changes in Land Use Land Cover (LULC) in the Gorgan River Basin (GRB) during the 1990–2020 period and predicted the changes by 2040. First, a change analysis employing satellite imagery from 1990 to 2020 was carried out. Then, the Multi-Layer Perceptron (MLP) technique was used to predict the transition potential. The accuracy rate, training RMS, and testing RMS of the artificial neural network, MLP, and the transition potential modeling were computed in order to evaluate the results. Utilizing projections for 2020, the prediction of land cover change was made. By contrasting the anticipated land cover map of 2020 with the actual land cover map of 2020, the accuracy of the model was evaluated. The LULC conditions in the future were predicted under two scenarios of the current change trend (scenario 1) and the ecological capability of the land (scenario 2) by 2040. Seven landscape metrics were considered, including Number of Patches, Patch Density, the Largest Patch Index, Edge Density, Land- scape Shape Index, Patch Area, and Area-Weighted Mean Shape Index. Based on the Cramer coefficient, the most critical factors affecting LULC change were elevation, distance from forest, and experimental probability of change. For the 1990–2020 period, the LULC change was shown to be influenced by deforestation, reduced rangeland, and expansion of agricultural and residential areas. Based on scenario 1, the area of forest, agriculture, and rangeland would face −0.8, 0.5, and 0.1% changes in the total area, respectively. In scenario 2, the area of forest, agriculture, and rangeland would change by 0.1, −1.3, and 1.3% of the total area, respectively. Landscape metrics results indicated the destructive trend of the landscape during the 1990–2020 period. For improving the natural condition of the GRB, it is suggested to prioritize different areas in need of regeneration due to inappropriate LULC changes and take preventive and protective measures where changes in LULC were predicted in the future, taking into account land management conditions (scenario 2)

Disaster Site Structure Analysis: Examining Effective Remote Sensing Techniques in Blue Tarpaulin Inspection

This thesis aimed to evaluate three methods of analyzing blue roofing tarpaulin (tarp) placed on homes in post natural disaster zones with remote sensing techniques by assessing the different methods- image segmentation, machine learning (ML), and supervised classification. One can determine which is the most efficient and accurate way of detecting blue tarps. The concept here was that using the most efficient and accurate way to locate blue tarps can aid federal, state, and local emergency management (EM) operations and homeowners. In the wake of a natural disaster such as a tornado, hurricane, thunderstorm, or similar weather events, roofs are the most likely to be damaged (Esri Events., 2019). Severe roof damage needs to be mitigated as fast as possible: which in the United States is often done at no cost by the Federal Emergency Management Agency (FEMA). This research aimed to find the most efficient and accurate way of detecting blue tarps with three different remote sensing practices. The first method, image segmentation, separates parts of a whole image into smaller areas or categories that correspond to distinct items or parts of objects. Each pixel in a remotely sensed image is then classified into categories set by the user. A successful segmentation will result when pixels in the same category have comparable multivariate, grayscale values and form a linked area, whereas nearby pixels in other categories have distinct values. Machine Learning, ML, a second method, is a technique that processes data depending on many layers for feature v identification and pattern recognition. ArcGIS Pro mapping software processes data with ML classification methods to classify remote sensing imagery. Deep learning models may be used to recognize objects, classify images, and in this example, classify pixels. The resultant model definition file or deep learning software package is used to run the inference geoprocessing tools to extract particular item positions, categorize or label the objects, or classify the pixels in the picture. Finally, supervised classification is based on a system in which a user picks sample pixel in an image that are indicative of certain classes and then tells image-processing software to categorize the other pixels in the picture using these training sites as references. To group pixels together, the user also specifies the limits for how similar they must be. The number of classifications into which the image is categorized is likewise determined by the user. The importance of tracking blue roofs is multifaceted. Structures with roof damage from natural disasters face many immediate dangers, such as further water and wind damage. These communities are at a critical moment as responding to the damage efficiently and effectively should occur in the immediate aftermath of a disaster. In part due to strategies such as FEMA and the United States Army Corps of Engineers’ (USACE) Operation Blue Roof, most often blue tarpaulins are installed on structures to prevent further damage caused by wind and rain. From a Unmanned Arial Vehicles (UAV) perspective, these blue tarps stand out amid the downed trees, devastated infrastructure, and other debris that will populate the area. Understanding that recovery can be one of the most important stages of Emergency Management, testing techniques vi for speed, accuracy, and effectiveness will assist in creating more effective Emergency Management (EM) specialists

Flash flood susceptibility assessment and zonation by integrating analytic hierarchy process and frequency ratio model with diverse spatial data

Flash floods are the most dangerous kinds of floods because they combine the destructive power of a flood with incredible speed. They occur when heavy rainfall exceeds the ability of the ground to absorb it. The main aim of this study is to generate flash flood maps using Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) models in the river’s floodplain between the Jhelum River and Chenab rivers. A total of eight flash flood-causative physical parameters are considered for this study. Six parameters are based on remote sensing images of the Advanced Land Observation Satellite (ALOS), Digital Elevation Model (DEM), and Sentinel-2 Satellite, which include slope, elevation, distance from the stream, drainage density, flow accumulation, and land use/land cover (LULC), respectively. The other two parameters are soil and geology, which consist of different rock and soil formations, respectively. In the case of AHP, each of the criteria is allotted an estimated weight according to its significant importance in the occurrence of flash floods. In the end, all the parameters were integrated using weighted overlay analysis in which the influence value of drainage density was given the highest weight. The analysis shows that a distance of 2500 m from the river has values of FR ranging from 0.54, 0.56, 1.21, 1.26, and 0.48, respectively. The output zones were categorized into very low, low, moderate, high, and very high risk, covering 7354, 5147, 3665, 2592, and 1343 km2, respectively. Finally, the results show that the very high flood areas cover 1343 km2, or 6.68% of the total area. The Mangla, Marala, and Trimmu valleys were identified as high-risk zones of the study area, which have been damaged drastically many times by flash floods. It provides policy guidelines for risk managers, emergency and disaster response services, urban and infrastructure planners, hydrologists, and climate scientists

Impacts of DEM Type and Resolution on Deep Learning-Based Flood Inundation Mapping

This paper presents a comprehensive study focusing on the influence of DEM type and spatial resolution on the accuracy of flood inundation prediction. The research employs a state-of-the-art deep learning method using a 1D convolutional neural network (CNN). The CNN-based method employs training input data in the form of synthetic hydrographs, along with target data represented by water depth obtained utilizing a 2D hydrodynamic model, LISFLOOD-FP. The performance of the trained CNN models is then evaluated and compared with the observed flood event. This study examines the use of digital surface models (DSMs) and digital terrain models (DTMs) derived from a LIDAR-based 1m DTM, with resolutions ranging from 15 to 30 meters. The proposed methodology is implemented and evaluated in a well-established benchmark location in Carlisle, UK. The paper also discusses the applicability of the methodology to address the challenges encountered in a data-scarce flood-prone region, exemplified by Pakistan. The study found that DTM performs better than DSM at lower resolutions. Using a 30m DTM improved flood depth prediction accuracy by about 21% during the peak stage. Increasing the resolution to 15m increased RMSE and overlap index by at least 50% and 20% across all flood phases. The study demonstrates that while coarser resolution may impact the accuracy of the CNN model, it remains a viable option for rapid flood prediction compared to hydrodynamic modeling approaches

Analysis of land use changes and water resources in lowland catchments of Northern Germany

With increasing pressures from natural disturbances and anthropogenic activities, water quality is degraded and sustainable management becomes more challenging. A good understanding of the key influential factors for water resources will help to develop effective catchment management plans for addressing water resources issues. However, a systematic assessment of cause-effect relationships between land use and water quality or quantity is still rare, particularly across different temporal and spatial scales. This study aims to explore the spatially distributed catchment variables controlling landscape patterns, and to identify the important catchment characteristics and spatial scales for explaining the water quality or quantity dynamics. The rural lowland catchments (namely Kielstau and Stör) in Northern Germany were selected as study areas. Intensive field campaigns have been carried out in the two catchments: land use mapping in both catchments and a water quality campaign (2018-2019) in the Stör catchment that complements campaigns from 1992-1994 and 2009-2011. Different multivariate statistics and a hydrological modelling (SWAT) approach have been applied. The distribution patterns of each specific land use class were identified based on logistic regression analysis using spatially distributed variables. Furthermore, stepwise multiple linear regression (SMLR) and redundancy analyses (RA) were applied to investigate influences of main categories of catchment characteristics on water quality at multiple spatial and temporal scales. The SWAT model was calibrated and validated for modeling the dynamic processes of streamflow, sediment, total phosphorus (TP), and total nitrogen (TN). The variabilities in main water balance components and nutrients in response to varied landscape patterns were investigated by applying the integrated approach of SWAT modeling and partial least squares regression model (PLSR)