Dynamicity of Land Use/Land Cover(LULC):An analysis from peri-urban and rural neighbourhoods of Durgapur Municipal Corporation(DMC)in India

The availability of better economic possibilities and well-connected transportation networks has attracted people to migrate to peri-urban and rural neighbourhoods,changing the landscape of regions outside the city and fostering the growth of physical infrastructure.Using multi-temporal satellite images,the dynamics of Land Use/Land Cover(LULC)changes,the impact of urban growth on LULC changes,and regional environmental implications were investigated in the peri-urban and rural neighbourhoods of Durgapur Municipal Corporation in India.The study used different case studies to highlight the study area’s heterogeneity,as the phenomenon of change is not consistent.Landsat TM and OLI-TIRS satellite images in 1991,2001,2011,and 2021 were used to analyse the changes in LULC types.We used the relative deviation(RD),annual change intensity(ACI),uniform intensity(UI)to show the dynamicity of LULC types(agriculture land;built-up land;fallow land;vegetated land;mining area;and water bodies)during 1991-2021.This study also applied the Decision-Making Trial and Evaluation Laboratory(DEMATEL)to measure environmental sensitivity zones and find out the causes of LULC changes.According to LULC statistics,agriculture land,built-up land,and mining area increased by 51.7,95.46,and 24.79 km^(2),respectively,from 1991 to 2021.The results also suggested that built-up land and mining area had the greatest land surface temperature(LST),whereas water bodies and vegetated land showed the lowest LST.Moreover,this study looked at the relationships among LST,spectral indices(Normalized Differenced Built-up Index(NDBI),Normalized Difference Vegetation Index(NDVI),and Normalized Difference Water Index(NDWI)),and environmental sensitivity.The results showed that all of the spectral indices have the strongest association with LST,indicating that built-up land had a far stronger influence on the LST.The spectral indices indicated that the decreasing trends of vegetated land and water bodies were 4.26 and 0.43 km^(2)/a,respectively,during 1991-2021.In summary,this study can help the policy-makers to predict the increasing rate of temperature and the causes for the temperature increase with the rapid expansion of built-up land,thus making effective peri-urban planning decisions.

Spatio-temporal behaviours of tropical cyclones over the bay of Bengal Basin in last five decades

Present research is an endeavour to scrutinise the spatio-temporal climatic characteristics of tropical cyclones(TCs)bustle in the Bay of Bengal basin,found in RSMC-IMD data all through 1971–2020.A large number of TCs,i.e.121 with a decadal average of 35.2 TCs has been examined for the last 50 years where depression(D)and deep depression(DD)have not been taken into account as these are less violent in nature.During the study periods,inter-annual and inter-decadal variation in cyclogenesis,landfall,length,speed,track shape and sinuosity,energy metrics and damage profile have been perceived.The study is clearly showing TCs took the northward track during the pre-monsoon season and made their landfall across the coasts of Bangladesh and Myanmar,while post-monsoon TCs made their landfall directly on the coasts of Orissa and West Bengal.In the post-monsoon phase,VF,ACE and PDI are significantly higher than in the monsoon season in the case of TCs and higher in the pre-monsoon season than in the monsoon season in the case of TCs comparing the energy metrics in different seasons.TC activity is comparatively pronounced during La Niña and El Niño regimes respectively and the genesis position in the BoB is moves to the east(west)of 87°E.During the cold regime,the number of extreme TC above the VSCS category,increased intensely.It is believed that the research findings will help stakeholders of the nation to take accurate strides to combat such violent events with persistent intensification.

Mapping the multi-hazards risk index for coastal block of Sundarban,India using AHP and machine learning algorithms

Global climate change,climate extremes,and overuse of natural resources are all major contributors to the risk brought on by cyclones.In I West Bengal state of India,the Pathar Pratima Block frequently experiences a variety of risks that result in significant loss of life and livelihood.In order to govern coastal society,it is crucial to measure and map the multi-hazards risk status.To depict the multi-hazards vulnerability and risk status,no cutting-edge models are currently being applied.Predicting distinct physical vulnerabilities is possible using a variety of cutting-edge machine learning techniques.This study set out to precisely describe multi-hazard risk using powerful machine learning methods.This study involved the use of Analytic Hierarchical Analysis and two cutting-edge machine-learning algorithms-Random Forest and Artificial Neural Network,which are yet underutilized in this area.The multi-hazards risk was determined by taking into account six criteria.The southern and eastern regions of the research area are clearly identified by the multi-hazards risk maps as having high to extremely high hazards risk levels.Cyclonic hazards and embankment breaching are the main dominant factors among the multi-hazards.The machine learning approach is the most accurate model for mapping the multi-hazards risk where the ROC result of Random forest and artificial neural network is more than the conventional method AHP.Here RF is the most validated model than the other two.The effectiveness,root mean square error,true skill statistics,Friedman and Wilcoxon rank test,and area under the curve of receiver operating characteristic tests were used to evaluate the prediction capacity of newly constructed models.The RMSE values of 0.24 and 0.26,TSS values of 0.82 and 0.73,and AUC values of 88.20%and 89.10%as produced by RF and ANN models,respectively,were all excellent.