Swapan Talukdar

The study sought to investigate the process of built-up expansion and the probability of built-up expansion in the English Bazar Block of West Bengal, India, using multitemporal Landsat satellite images and an integrated machine learning algorithm and fuzzy logic model. The land use and land cover (LULC) classification were prepared using a support vector machine (SVM) classifier for 2001, 2011, and 2021. The landscape fragmentation technique using the landscape fragmentation tool (extension for ArcGIS software) and frequency approach were proposed to model the process of built-up expansion. To create the built-up expansion probability model, the dominance, diversity, and connectivity index of the built-up areas for each year were created and then integrated with fuzzy logic. The results showed that, during 2001-2021, the built-up areas increased by 21.67%, while vegetation and water bodies decreased by 9.28 and 4.63%, respectively.

Groundwater scarcity is one of the most concerning issues in arid
and semi-arid regions. In this study, we develop and validate a
novel artificial intelligence that is a coupling of five ensemble
benchmark algorithms e.g., artificial neural network (ANN),
reduced-error pruning trees (REPTree), radial basis function (RBF),
M5P and random forest (RF) with particle swarm optimization
(PSO) for delineating GWP zones. Further, nine parameters used
for the GWP modelling and to test and train the proposed PSObased
models. Additionally, this study proposes a receiver operating
characteristic (ROC) based sensitivity analysis for GWP modelling.
Multicollinearity test, information gain ratio, and correlation
attribute evaluation methods used to choose important parameters
for the proposed GWP model. The result shows that drainage
density, elevation, and land use/land cover have a higher influence
on the GWP using correlation attribute evaluation methods.
Results showed that the hybrid PSO-RF model performed better
than other proposed hybrid models.

The flooding in Bangladesh during monsoon season is very common and frequently happens. Consequently, people have been experiencing tremendous damage to properties, infrastructures, and human casualties. Usually, floods are one of the devastating disasters from nature, but for developing nations like Bangladesh, flooding becomes worse. Due to the dynamic and complex nature of the flooding, the prediction of flooding sites was usually very difficult for flood management. But the artificial intelligence and advanced remote sensing techniques together could predict and identify the possible sites, which are vulnerable to flooding. The present work aimed to predict and identify the flooding sites or flood susceptible zones in the Teesta River basin by employing state-of-the-art novel ensemble machine learning algorithms. We developed ensembles of bagging with REPtree, random forest (RF), M5P, and random tree (RT) algorithms for obtaining reliable and highly accurate results. Twelve factors, which are considered as the conditioning factors, and 413 current and former flooding points were identified for flooding susceptibility modelling. The Information Gain ratio statistical technique was utilized to determine the influence of the factors for flooding. We applied receiver operating characteristic curve (ROC) for validation of the flood susceptible models. The Freidman test, Wilcoxon signed-rank test, Kruskal-Wallis test and Kol-mogorov-Smirnov test were applied together for the first time in flood susceptibility modelling to compare the models with each other. Results showed that more than 800 km 2 area was predicted as the very high flood susceptibility zones by all algorithms. The ROC curve showed that all models achieved more than 0.85 area under the curve indicating highly accurate flood models. For flood susceptibility modelling, the bagging with M5P performed superior, followed by bagging with RF, bagging with REPtree and bagging with RT. The methodology and solution-oriented results presented in this paper will assist the regional as well as local authorities and the policy-makers for mitigating the risks related to floods and also help in developing appropriate measures to avoid potential damages. Electronic supplementary material The online version of this article (https://doi.