3 research outputs found
Consumption, Commitmants and Preferences for Risk
No full textWe examine an economy in which the cost of consuming some goods can be reduced by making commitments to consumption levels independent of the state. For example, it is cheaper to produce housing services via owner-occupied than rented housing, but the transactions costs associated with the former prompt relatively inflexible housing consumption paths. We show that consumption commitments can cause risk-neutral consumers to care about risk, creating incentives to both insure risks and bunch uninsured risks together. For example, workers may prefer to avoid wage risk while bearing an unemployment risk that is concentrated in as few states as possible.
Soil erosion and sediment yield estimation in a tropical monsoon dominated river basin using GIS-based models
No full textThe increasing soil erosion (SE) and the associated problems for society, economy, and environment sparked a lot of interest in estimating and mapping SE at different basin scales. The estimation of SE exhibits that SE ranges from 10 to 50 t ha−1 yr−1, with a mean SE of 20 t ha−1 yr−1. The very steep slopes account for 54.21% of total soil loss. The SRB areas where soil loss rates are >10 t ha−1 yr−1 are considered the target areas which account for 27% of the study area and 96% of the soil loss). The high SY is concentrated only in the first-order basins located in a higher slope zone in the northern part of the river. Besides, basin morphometry (basin shape, relative relief) and anthropogenic activities (agricultural land) are retained in the PSLR model as significant factors contributing to SY in the entire river basin.</p
Table1_Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach.DOCX
No full textThis study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood susceptible areas of low altitudinal range, humid subtropical fluvial floodplain environment of the Middle Ganga Plain (MGP). This part of the MGP, which is in the central Ganga River Basin (GRB), is experiencing worse floods in the changing climatic scenario causing an increased level of loss of life and property. The MGP experiencing monsoonal subtropical humid climate, active tectonics induced ground subsidence, increasing population, and shifting landuse/landcover trends and pattern, is the best natural laboratory to test all the susceptibility prediction genre of models to achieve the choice of best performing model with the constant number of input parameters for this type of topoclimatic environmental setting. This will help in achieving the goal of model universality, i.e., finding out the best performing susceptibility prediction model for this type of topoclimatic setting with the similar number and type of input variables. Based on the highly accurate flood inventory and using 12 flood predictors (FPs) (selected using field experience of the study area and literature survey), two machine learning (ML) ensemble models developed by bagging frequency ratio (FR) and evidential belief function (EBF) with classification and regression tree (CART), CART-FR and CART-EBF, were applied for flood susceptibility zonation mapping. Flood and non-flood points randomly generated using flood inventory have been apportioned in 70:30 ratio for training and validation of the ensembles. Based on the evaluation performance using threshold-independent evaluation statistic, area under receiver operating characteristic (AUROC) curve, 14 threshold-dependent evaluation metrices, and seed cell area index (SCAI) meant for assessing different aspects of ensembles, the study suggests that CART-EBF (AUCSR = 0.843; AUCPR = 0.819) was a better performant than CART-FR (AUCSR = 0.828; AUCPR = 0.802). The variability in performances of these novel-advanced ensembles and their comparison with results of other published models espouse the need of testing these as well as other genres of susceptibility models in other topoclimatic environments also. Results of this study are important for natural hazard managers and can be used to compute the damages through risk analysis.</p
