How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region.

Although the effect of digital elevation model (DEM) and its spatial resolution on flood simulation modeling has been well studied, the effect of coarse and finer resolution image and DEM data on machine learning ensemble flood susceptibility prediction has not been investigated, particularly in data sparse conditions. The present work was, therefore, to investigate the performance of the resolution effects, such as coarse (Landsat and SRTM) and high (Sentinel-2 and ALOS PALSAR) resolution data on the flood susceptible models. Another motive of this study was to construct very high precision and robust flood susceptible models using standalone and ensemble machine learning algorithms. In the present study, fifteen flood conditioning parameters were generated from both coarse and high resolution datasets. Then, the ANN-multilayer perceptron (MLP), random forest (RF), bagging (B)-MLP, B-gaussian processes (B-GP) and B-SMOreg algorithms were used to integrate the flood conditioning parameters for generating the flood susceptible models. Furthermore, the influence of flood conditioning parameters on the modelling of flood susceptibility was investigated by proposing an ROC based sensitivity analysis. The validation of flood susceptibility models is also another challenge. In the present study, we proposed an index of flood vulnerability model to validate flood susceptibility models along with conventional statistical techniques, such as the ROC curve. Results showed that the coarse resolution based flood susceptibility MLP model has appeared as the best model (area under curve: 0.94) and it has predicted 11.65 % of the area as very high flood susceptible zones (FSz), followed by RF, B-MLP, B-GP, and B-SMOreg. Similarly, the high resolution based flood susceptibility model using MLP has predicted 19.34 % of areas as very high flood susceptible zones, followed by RF (14.32 %),B-MLP (14.88 %), B-GP, and B-SMOreg. On the other hand, ROC based sensitivity analysis showed that elevation influences flood susceptibility largely for coarse and high resolution based models, followed by drainage densityand flow accumulation. In addition, the accuracy assessment using the IFV model revealed that the MLP model outperformed all other models in the case of a high resolution imageThe coarser resolution image's performance level is acceptable but quite low. So, the study recommended the use of high resolution images for developing a machine learning algorithm based flood susceptibility model. As the study has clearly identified the areas of higher flood susceptibility and the dominant influencing factors for flooding, this could be used as a good database for flood management.

Measuring dam induced alteration in water richness and eco-hydrological deficit in flood plain wetland.

Along with wetland loss, the damming effect on hydrological modification in wetland is another less debated and challenging topic, which needs to have urgent attention. The present work intended to investigate the damming effect on the water richness and eco-hydrological condition of the floodplain wetland and its consequent ecological responses in Punarbhaba River Basin of India and Bangladesh. Satellite images derived hydro-period, water presence frequency (WPF), and water depth were generated for developing water richness model in pre (up to 1992) and post dam conditions (1993-2019). The range of variability (RVA) was modelled using time series satellite images based water index or normalized difference water index (NDWI). Based on RVA model, the hydrological failure rate was developed. Depth of water was used for preparing the flow duration curve (FDC) to estimate the eco-hydro-deficit and surplus condition in wetland at spatial scale for pre and post-dam periods. Satellite image based trophic state index models for pre and post dam conditions were constructed to investigate the ecological response of dam on floodplain wetlands. Results of water richness model showed that wetlands area under high wetland water richness zone decreased from 71.83% to 7.65% in the post-dam conditions. Results of hydrological failure rate showed that high failure rate captured 45% of total wetland area in the post-dam period. Results of eco-hydro-deficit exhibited that eco-hydro-deficit areas increased from 11.22% to 52.19% and 35.03%-52.67% respectively in post-dam conditions indicating growing ecological stress. The TSI models showed that most parts of the wetlands were converted from oligotrophic to meso-eutrophic state signifying the qualitative degradation of water and potential ecosystem health. The area under high TSI was observed in the wetland area having eco-hydro-deficit and high hydrological failure rate zones. These characteristics of wetland areas were found at the fringe of wetlands and fragmented smaller wetland units. The study concluded that damming over the Punarbhaba River adversely affected the water security of the floodplain wetlands in terms of modifying the hydrological richness, ecological condition of the wetland habitat, and ecological systems. The findings of the present study could provide a comprehensive research on the monitoring of surface water crisis in the wetlands, which will be the basic foundation to formulate water resource management plans for conservation, management and restoration of wetlands.