Predicting flood insurance claims with hydrologic and socioeconomic demographics via machine learning: exploring the roles of topography, minority populations, and political dissimilarity

Current research on flooding risk often focuses on understanding hazards, de-emphasizing the complex pathways of exposure and vulnerability. We investigated the use of both hydrologic and social demographic data for flood exposure mapping with Random Forest (RF) regression and classification algorithms trained to predict both parcel- and tract-level flood insurance claims within New York State, US. Topographic characteristics best described flood claim frequency, but RF prediction skill was improved at both spatial scales when socioeconomic data was incorporated. Substantial improvements occurred at the tract-level when the percentage of minority residents, housing stock value and age, and the political dissimilarity index of voting precincts were used to predict insurance claims. Census tracts with higher numbers of claims and greater densities of low-lying tax parcels tended to have low proportions of minority residents, newer houses, and less political similarity to state level government. We compared this data-driven approach and a physically-based pluvial flood routing model for prediction of the spatial extents of flooding claims in two nearby catchments of differing land use. The floodplain we defined with physically based modeling agreed well with existing federal flood insurance rate maps, but underestimated the spatial extents of historical claim generating areas. In contrast, RF classification incorporating hydrologic and socioeconomic demographic data likely overestimated the flood-exposed areas. Our research indicates that quantitative incorporation of social data can improve flooding exposure estimates

Spatiotemporal variability of urban growth factors: A global and local perspective on the megacity of Mumbai

The rapid growth of megacities requires special attention among urban planners worldwide, and partic-ularly in Mumbai, India, where growth is very pronounced. To cope with the planning challenges this willbring, developing a retrospective understanding of urban land-use dynamics and the underlying driving-forces behind urban growth is a key prerequisite. This research uses regression-based land-use changemodels – and in particular non-spatial logistic regression models (LR) and auto-logistic regression mod-els (ALR) – for the Mumbai region over the period 1973–2010, in order to determine the drivers behindspatiotemporal urban expansion. Both global models are complemented by a local, spatial model, the so-called geographically weighted logistic regression (GWLR) model, one that explicitly permits variationsin driving-forces across space. The study comes to two main conclusions. First, both global models suggestsimilar driving-forces behind urban growth over time, revealing that LRs and ALRs result in estimatedcoefficients with comparable magnitudes. Second, all the local coefficients show distinctive temporaland spatial variations. It is therefore concluded that GWLR aids our understanding of urban growth pro-cesses, and so can assist context-related planning and policymaking activities when seeking to secure asustainable urban future

Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model

Several factors contribute to on-going challenges of spatial planning and urban policy in megacities, including rapid population shifts, less organized urban areas, and a lack of data with which to monitor urban growth and land use change. To support Mumbai's sustainable development, this research was conducted to examine past urban land use changes on the basis of remote sensing data collected between 1973 and 2010. An integrated Markov Chains–Cellular Automata (MC–CA) urban growth model was implemented to predict the city's expansion for the years 2020–2030. To consider the factors affecting urban growth, the MC–CA model was also connected to multi-criteria evaluation to generate transition probability maps. The results of the multi-temporal change detection show that the highest urban growth rates, 142% occurred between 1973 and 1990. In contrast, the growth rates decreased to 40% between 1990 and 2001 and decreased to 38% between 2001 and 2010. The areas most affected by this degradation were open land and croplands. The MC–CA model predicts that this trend will continue in the future. Compared to the reference year, 2010, increases in built-up areas of 26% by 2020 and 12% by 2030 are forecast. Strong evidence is provided for complex future urban growth, characterized by a mixture of growth patterns. The most pronounced of these is urban expansion toward the north along the main traffic infrastructure, linking the two currently non-affiliated main settlement ribbons. Additionally, urban infill developments are expected to emerge in the eastern areas, and these developments are expected to increase urban pressure

How much can temporally stationary factors explain cellular automata-based simulations of past and future urban growth?

Driving factors are usually assumed temporally stationary in cellular automata (CA)based land use modeling, hence the persistence of their relationships. Therefore, major questions as to how much do the temporally stationary factors explain the past and future urban growth, and how long can these factors justify the projection of urban scenarios in the future, are worth further study. We selected seven explanatory driving factors to calibrate a DE-CA (differential evolution-based CA)model to simulate urban growth in Ningbo of China during 2000–2015 and project nine scenarios of urban growth from 2015 to 2060. We evaluated the effects of factors on urban growth using generalized additive models (GAM)based on fitting statistics such as accumulative deviance explained (ADE). Our results show remarkably temporal change in factor effects on the future urban growth – the ADE peaks with 34.7% in 2045 for the total projected urban growth since 2015 while that for every five years decreases continuously from 26.5% during 2000–2005 to 1.9% during 2050–2055, but slightly increase to 3.0% during 2055–2060. These indicate that the stationary factors have less strong explanatory power to the new urban areas that are farther away from the existing built-up areas. The results suggest that a 30-year period in the future is most suitable to project the urban growth scenarios, where the new urban area approximates the initial urban area. The specific best period for scenario projection elsewhere can then be identified using the method presented in this study

Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: A case study.

The majority of cities are rapidly growing. This makes the monitoring and modeling of urban change’s spatial patterns critical to urban planners, decision makers, and environment protection activists. Although a wide range of methods exists for modeling and simulating urban growth, machine learning (ML) techniques have received less attention despite their potential for producing highly accurate predictions of future urban extents. The aim of this study is to investigate two ML techniques, namely radial basis function network (RBFN) and multi-layer perceptron (MLP) networks, for modeling urban change. By predicting urban change for 2010, the models’ performance is evaluated by comparing results with a reference map and by using a set of pertinent statistical measures, such as average spatial distance deviation and figure of merit. The application of these techniques employs the case study area of Mumbai, India. The results show that both models, which were tested using the same explanatory variables, produced promising results in terms of predicting the size and extent of future urban areas. Although a close match between RBFN and MLP is observed, RBFN demonstrates higher spatial accuracy of prediction. Accordingly, RBFN was utilized to simulate urban change for 2020 and 2030. Overall, the study provides evidence that RBFN is a robust and efficient ML technique and can therefore be recommended for land use change modeling

Modeling the Spatial Variation of Urban Land Surface Temperature in Relation to Environmental and Anthropogenic Factors: A Case Study of Tehran, Iran

Spatial variation of Urban Land Surface Temperature (ULST) is a complex function of environmental, climatic, and anthropogenic factors. It thus requires specific techniques to quantify this phenomenon and its influencing factors. In this study, four models, Random Forest (RF), Generalized Additive Model (GAM), Boosted Regression Tree (BRT), and Support Vector Machine (SVM), are calibrated to simulate the ULST based on independent factors, i.e., land use/land cover (LULC), solar radiation, altitude, aspect, distance to major roads, and Normalized Difference Vegetation Index (NDVI). Additionally, the spatial influence and the main interactions among the influential factors of the ULST are explored. Landsat-8 is the main source for data extraction and Tehran metropolitan area in Iran is selected as the study area. Results show that NDVI, LULC, and altitude explained 86% of the ULST C variation. Unexpectedly, lower LST is observed near the major roads, which was due to the presence of vegetation along the streets and highways in Tehran. The results also revealed that variation in the ULST was influenced by the interaction between altitude - NDVI, altitude - road, and LULC - altitude. This indicates that the individual examination of the underlying factors of the ULST variation might be unilluminating. Performance evaluation of the four models reveals a close performance in which their R2 and Root Mean Square Error (RMSE) fall between 60.6-62.1% and 2.56-2.60 C, respectively. However, the difference between the models is not statistically significant. This study evaluated the predictive performance of several models for ULST simulation and enhanced our understanding of the spatial influence and interactions among the underlying driving forces of the ULST variations

Multiple-depth modeling of soil organic carbon using visible–near infrared spectroscopy

This paper evaluates the capability of visible-near-infrared (VIS-NIR) spectroscopy to estimate soil organic carbon (SOC) at multiple depths including 0–15, 15–40, 40–60, and 60–80 cm. Four modeling algorithms, namely partial least squares regression (PLSR), principal component regression (PCR), support vector regression (SVR), and random forest (RF) were implemented calibrated to process the spectroscopy data. Overall, 120 soil samples were taken from 30 profiles at the depth of 0–80 cm. We implemented the four models considering different pre-processing techniques including Savitzky-Golay first deviation (SGD), normalization (N), and standard normal variate transformation (SNV). Results revealed that the RF model outperformed other models and the highest accuracy was reached with no pre-processing for all depths excluding 40–60 cm, where the R2 and RMSE were between 0.55–0.77 and 0.75–0.84% respectively. For the depth of 40–60  cm, the maximum accuracy was observed when SGD pre-processing was applied, resulting in R2=0.73 and RMSE = 0.78%. Generally, our findings indicate that the spectral data can provide useful information to predict SOC at multiple depths

USING MULTIVARIATE ADAPTIVE REGRESSION SPLINE AND ARTIFICIAL NEURAL NETWORK TO SIMULATE URBANIZATION IN MUMBAI, INDIA

Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets. Non-parametric models are data driven and usually do not have a fixed model structure or model structure is unknown before the modelling process. On the other hand, global models perform modelling using all the available data. In addition, parametric models have a fixed structure before the modelling process and they are model driven. Since few studies have compared local non-parametric models with global parametric models, this study compares a local non-parametric model called multivariate adaptive regression spline (MARS), and a global parametric model called artificial neural network (ANN) to simulate urbanization in Mumbai, India. Both models determine the relationship between a dependent variable and multiple independent variables. We used receiver operating characteristic (ROC) to compare the power of the both models for simulating urbanization. Landsat images of 1991 (TM) and 2010 (ETM+) were used for modelling the urbanization process. The drivers considered for urbanization in this area were distance to urban areas, urban density, distance to roads, distance to water, distance to forest, distance to railway, distance to central business district, number of agricultural cells in a 7 by 7 neighbourhoods, and slope in 1991. The results showed that the area under the ROC curve for MARS and ANN was 94.77% and 95.36%, respectively. Thus, ANN performed slightly better than MARS to simulate urban areas in Mumbai, India

Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors

Mapping the distribution and type of land use and land cover (LULC) is essential for watershed management. The Tigris-Euphrates basin is a transboundary region in the Middle East shared between six countries, but a recent fine-scale LULC map of the area is lacking. Using Landsat-8 time series, a 30-m resolution LULC map was produced for the Tigris-Euphrates basin. In total, 1184 Landsat scenes were processed within the Google Earth Engine (GEE). For the collection of ground truth data, differential manifestations of green cover were considered by dividing the study area into five climatic regions and the training samples were taken from each sub-region. To account for the temporal variation of LULC types, six two-month interval composite layers, including the spectral and thermal bands of Landsat-8, texture and spectral indices, as well as topographic factors were created for the target year 2019. Image segmentation and classification were performed using the simple non-iterative clustering (SNIC) and Random Forest (RF) algorithms, respectively. A computationally effective parallel processing approach was developed, which created a number of tiles and sub-tiles and a bulk command was converted into smaller parallel commands. The generated LULC map showed a satisfactory overall accuracy of 91.7%, with the highest User’s accuracy in water and wetland, and the lowest in rainfed crop and rangeland and the highest Producer’s accuracy in water and barren areas, and the lowest in garden and rangeland. This study highlights the necessity of using multi-temporal data for LULC mapping, in particular, multi-temporal NDVI, for the separation of different green cover types in arid and semi-arid environment