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Gully erosion is one of the advanced forms of water erosion. Identifying the effective factors and gully erosion predicting is one of the important tools to control and manage such phenomenon. The main purpose of this study is to evaluate the effect of four different resampling algorithms including cross-validation (5-fold and 10-fold) and bootstrapping (Bootstrap and Optimism bootstrap) on boosted regression tree (BRT), support vector machine (SVM), and random forest (RF) models in spatial modeling and evaluation of head-cut gully erosion in Konduran watershed. For this purpose, based on an extensive field survey, the points of the head-cut of the gully erosion were identified first, and a map of the distribution of head-cut gully erosion in the study area was prepared. Then 18 variable identify and prepare as factors affecting the occurrence of head-cut gully erosion. To assess the efficiency of the models, receiver operating characteristics (ROC) and area under the curve (AUC) were used. The results of the assessment indicated that the use of resampling algorithms increases the efficiency of the models. The integrated optimism-bootstrap-BRT, optimism-bootstrap-SVM, and Optimism-Bootstrap-RF models with AUC 0.85, 0.823 and 0.89 respectively, outperformed the cross-validation 5fold (BRT, SVM, RF), Cross-validation 10fold (BRT, SVM, RF) and Bootstrap (BRT, SVM, RF) integrated algorithms.

Rapid urban proliferation is an indispensable and reciprocal issue in contemporary urban planning and development. This study envisages the prediction-adaptation-resilience (PAR) approach to analyze the future urban landscape resilience and sustainable development goals (SDGs). We have selected a small, unplanned growing up city, namely, Krishnanagar urban agglomeration (KUA), in India, to apply the PAR approach. Therefore, land use land cover map has been prepared for 2000, 2010, and 2020. The result shows the built-up area has been increased most in past 20 years, from 6.36 km2 to 13.23 km2. Then, the cellular automata-Markov chain model is applied to predict the future potential urban development surface for 2030 and 2040. The receiver operating characteristic (ROC) curve shows 83.6% success rate between the predicted and actual map of CA-Markov. The prediction map of 2030 and 2040 shows that the built-up area continuously expands (13.23 km2 to 16.52 km2) towards KUA's surrounding regions. Consequently, other decreasing land classes will be a threat to SDGs and urban resilience. So, people of KUA are adopting the changing hostile nature of urbanisation and urban vulnerability. Hence, this study will help the local administration to make a proper urban planning and adaptation strategies by maintaining good urban governance to achieve 8 SDGs of UN's 2030 Agenda in future.

Assessment of flood risk zonation and landscape vulnerability to flood are fundamental aspects in flood risk management. Landsat 8 Operational Land Imager (OLI) and ASTER DEM data were used to assess landscape vulnerability to flood inundation and flood risk in Malda district of West Bengal state, India. Flood inundation map was prepared on the basis of water and non-water pixels on images (before and during the flood event). Flood risk map was prepared using equal interval of separation based on elevation and inundated flooded area. Flood inundation map was overlaid on the pre-monsoon land use/land cover map to produce landscape vulnerability to flood. The results revealed that 19% area of the district was flooded during monsoon flood event in 2014 and the agricultural area was most affected land use, sharing 62% of the total flood affected area, followed by river bed (21%), built up (7%) and vegetation (5%). The flood risk map of the district shows that temporary river islands, sand banks along the Ganga river course lie in low flood plain and were considered under high risk zone. The flood plain alongside minor stream drainage in southern, north-western and in between them comes under medium flood risk zones. Flood risk is low in areas which are away from the rivers. Non flooded areas were identified in high lands of eastern region of the district. Landscape vulnerability map shows that the blocks located along the Ganga river namely Kaliachak I, Kaliachak II, Kaliachak III, Manikchak, Ratua I were highly vulnerable to flood. The study suggests that efforts should be made to remove the sediments for increasing the depth of river. Spurs and bed bars should be constructed to avoid great loss of prime agricultural land, property and lives of people.