Browsing by Author "Sarkar, Showmitra Kumar"

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Artificial Neural Network-Based Land Use-Specific Carbon Patterns and Their Effects on Land Surface Temperature as a Result of the Rohingya Refugee Influx
(IEEE, 2023-12-21) Sarkar, Showmitra Kumar; Saroar, MD. Mustafa; Das, Palash Chandra; Chakraborty, Tanmoy; Rudra, Rhyme Rubayet; Alam, Edris; Islam, MD. Kamrul; Islam, Abu Reza Md. Towfiqul
The objective of the research is to investigate how refugees’ influx has altered the carbon dynamics of different land uses and the relationship between land use specific carbon emissions and land surface temperature (LST). Two upazilais of the Cox’s Bazar district, Bangladesh (i.e., Ukhiya and Teknaf), were mostly affected by the Rohingya refugee influx and are the focus of the study. The study classified the land use land cover (LULC) into four classes (e.g., agricultural, forest, settlement, and water) for two different time periods (i.e., before and after the influx of Rohingya refugees) using an artificial neural network algorithm and sentinel satellite imagery. Carbon emissions and absorptions specific to land use were calculated using classified land use land cover and coefficients. Again, two time series of Landsat 8 imagery were applied to estimate land surface temperature shifts. The area of forests was found to have decreased by 21.19 square miles (9.58 percent) and the area of settlements to have increased by 18.24 square miles (8.25 percent) between 2017 and 2021. There was a negative net land-use based carbon emission of -5187.02 tons per year in 2017. In 2021, it was predicted that annual net emissions would total 2208.24 tons. LST during the study period has increased as a result of human activities that release greenhouse gases into the atmosphere. The findings of this research will inform policymakers’ decisions about the conservation and sustainable development of natural resources in the region experiencing an influx of Rohingya refugees.
Coupling of Machine Learning and Remote Sensing for Soil Salinity Mapping in Coastal Area of Bangladesh
(Springer, 2023-10-10) Sarkar, Showmitra Kumar; Rudra, Rhyme Rubayet; Sohan, Abid Reza; Das, Palash Chandra; Ekram, Khondaker Mohammed Mohiuddin; Talukdar, Swapan; Rahman, Atiqur; Alam, Edris; Islam, Md Kamrul; Islam, Abu Reza Md. Towfiqul
Soil salinity is a pressing issue for sustainable food security in coastal regions. However, the coupling of machine learning and remote sensing was seldom employed for soil salinity mapping in the coastal areas of Bangladesh. The research aims to estimate the soil salinity level in a southwestern coastal region of Bangladesh. Using the Landsat OLI images, 13 soil salinity indicators were calculated, and 241 samples of soil salinity data were collected from a secondary source. This study applied three distinct machine learning models (namely, random forest, bagging with random forest, and artificial neural network) to estimate soil salinity. The best model was subsequently used to categorize soil salinity zones into five distinct groups. According to the findings, the artificial neural network model has the highest area under the curve (0.921), indicating that it has the most potential to predict and detect soil salinity zones. The high soil salinity zone covers an area of 977.94 km2 or roughly 413.51% of the total study area. According to additional data, a moderate soil salinity zone (686.92 km2) covers 30.56% of Satkhira, while a low soil salinity zone (582.73 km2) covers 25.93% of the area. Since increased soil salinity adversely affects human health, agricultural production, etc., the study's findings will be an effective tool for policymakers in integrated coastal zone management in the southwestern coastal area of Bangladesh.
Delineating the drought vulnerability zones in Bangladesh
(Scopus, 2024-10-26) Sarkar, Showmitra Kumar; Das, Swadhin; Rudra, Rhyme Rubayet; Ekram, Khondaker Mohammed Mohiuddin; Haydar, Mafrid; Alam, Edris; Islam, Md Kamrul
The research aims to explore the vulnerability of Bangladesh to drought by considering a comprehensive set of twenty-four factors, classified into four major categories: meteorological, hydrological, agricultural, and socioeconomic vulnerability. To achieve this, the study utilized a knowledge-based multi-criteria method known as the Analytic Hierarchy Process (AHP) to delineate drought vulnerability zones across the country. Weight estimation was accomplished by creating pairwise comparison matrices for factors and different types of droughts, drawing on relevant literature, field experience, and expert opinions. Additionally, online-based interviews and group discussions were conducted with 30 national and foreign professionals, researchers, and academics specializing in drought-related issues in Bangladesh. Results from overall drought vulnerability map shows that the eastern hills region displays a notably high vulnerability rate of 56.85% and an extreme low vulnerability rate of 0.03%. The north central region shows substantial vulnerability at high levels (35.85%), while the north east exhibits a significant proportion (41.68%) classified as low vulnerability. The north west region stands out with a vulnerability rate of 40.39%, emphasizing its importance for drought management strategies. The River and Estuary region displays a modest vulnerability percentage (38.44%), suggesting a balanced susceptibility distribution. The south central and south east regions show significant vulnerabilities (18.99% and 39.60%, respectively), while the south west region exhibits notable vulnerability of 41.06%. The resulting model achieved an acceptable level of performance, as indicated by an area under the curve value of 0.819. Policymakers and administrators equipped with a comprehensive vulnerability map can utilize it to develop and implement effective drought mitigation strategies, thereby minimizing the losses associated with drought.
Future Groundwater Potential Mapping Using Machine Learning Algorithms and Climate Change Scenarios in Bangladesh
(Springer Nature, 2024-05-06) Sarkar, Showmitra Kumar; Rudra, Rhyme Rubayet; Talukdar, Swapan; Das, Palash Chandra; Nur, Md. Sadmin; Alam, Edris; Islam, Md Kamrul; Islam, Abu Reza Md. Towfiqul
The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study’s results to inform evidence-based choices on water management and planning at the national level.
Groundwater Level Fluctuations and Associated Influencing Factors in Rangpur District, Bangladesh, Using Modified Mann-Kendall and GIS-Based AHP Technique
(Springer Nature, 2023-06-22) Monir, Md. Moniruzzaman; Sarker, Subaran Chandra; Sarkar, Showmitra Kumar; Ahmed, Mohd.; Mallick, Javed; Islam, Abu Reza Md. Towfiqul
Analysis of groundwater level fluctuations is critical to understanding groundwater system dynamics and the factors that cause groundwater level fluctuations. Although most of the earlier studies focused on time series trend analysis using typical non-parametric tests, groundwater level fluctuations in drought-prone areas of northern Bangladesh are still poorly understood. To this end, the present study aims to analyze groundwater level fluctuations from 1980 to 2019 and associated influencing factors on groundwater level oscillation using the modified Mann-Kendall test and Pearson’s correlation method. Using GIS-based analytical hierarchical process (AHP) techniques, this study also identifies potential groundwater zones in the designated area. The result showed that 42.5% of monitoring wells had groundwater levels from 4.5 to 5.5 m during the pre-monsoon, whereas 57.5% had groundwater levels from 2.5 to 3.5 m during the monsoon in recent years. Based on the annual average groundwater level, the result showed that 62.5% of monitored wells had a declining trend, and 37.5% had an increasing trend from 2000 to 2019. A negative correlation was observed between groundwater level and rainfall in all monitoring wells, as the area’s rainfall impacted the groundwater level fluctuations. There was also a substantial positive correlation between groundwater levels, extraction, and potential evapotranspiration. The groundwater potential zone mapping shows three primary groundwater prospect categories: poor (1.9%), moderate (34.16%), and excellent (63.94%). The findings will assist planners and policymakers in allocating groundwater resources in various sectors such as agriculture, drinking water, and industry.