Browsing by Author "Sonali Kundu"

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Comprehensive Drought Vulnerability Assessment in Northwestern Odisha: A Fuzzy Logic and Analytical Hierarchy Process Integration Approach
(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Susanta Mahato; Gita Mandal; Barnali Kundu; Sonali Kundu; P.K. Joshi; Pankaj Kumar
Crafting a comprehensive strategy to mitigate the impact of droughts, a complex geo-hazard profoundly affecting socio-economic aspects, entails the creation of a drought vulnerability map as a primary step. This study harmonizes geospatial techniques and the Fuzzy Analytical Hierarchy Process (fuzzy AHP) to formulate such a map for northwestern Odisha, India. From six principal drought-induced vulnerability parameters, namely physical attributes, water demand and usage, agriculture, land use, groundwater and population/development, 22 sub-parameters were selected. Spatial layers were generated for each sub-parameter, followed by their fuzzification using a fuzzy membership approach. Subsequently, AHP was employed to establish parameter weights through pair-wise comparisons. By applying the weighted overlay method, drought vulnerability maps were generated, classifying regions into five vulnerability levels: very high, high, moderate, low, and very low. The outcomes indicate that roughly 33% of the area is classified as having high drought vulnerability. Validation of the approach using statistical metrics, including accuracy, root mean square error and mean absolute error, demonstrates its efficacy in gauging drought vulnerability, thereby aiding planners in devising effective drought mitigation strategies. © 2023 by the authors.
Enhancing drought resilience: machine learning–based vulnerability assessment in Uttar Pradesh, India
(Springer, 2024) Barnali Kundu; Narendra Kumar Rana; Sonali Kundu
Drought is a natural and complex climatic hazard. It has both natural and social connotations. The purpose of this study is to use machine learning methods (MLAs) for drought vulnerability (DVM) in Uttar Pradesh, India. There were 18 factors used to determine drought vulnerability, separated into two groups: physical drought and meteorological drought. The study found that the eastern part of Uttar Pradesh is high to very highly prone to drought, which is approximately 31.38% of the area of Uttar Pradesh. The receiver operating characteristic curve (ROC) was then used to evaluate the machine learning models (artificial neural networks). According to the findings, the ANN functioned with AUC values of 0.843. For policy actions to lessen drought sensitivity, DVMs may be valuable. Future exploration may involve refining machine learning algorithms, integrating real-time data sources, and assessing the socio-economic impacts to continually enhance the efficacy of drought resilience strategies in Uttar Pradesh. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Integration of SPEI and machine learning for assessing the characteristics of drought in the middle ganga plain, an agro-climatic region of India
(Springer, 2024) Barnali Kundu; Narendra Kumar Rana; Sonali Kundu; Devendra Soren
Drought, as a natural and intricate climatic phenomenon, poses challenges with implications for both natural ecosystems and socioeconomic conditions. Evaluating the characteristics of drought is a significant endeavor aimed at mitigating its impact on society and individuals. This research paper explores the integration of the Standardized Precipitation Evapotranspiration Index (SPEI) and machine learning techniques for an assessment of drought characteristics in the Middle Ganga Plain, a crucial agro-climatic region in India. The study focuses on evaluating the frequency, intensity, magnitude, and recurrence interval of drought events. Various drought models, including Random Forest (RF), Artificial Neural Networks (ANN), and an ensemble model combining ANN and RF, were employed to analyze and predict drought patterns at different temporal scales (3-month, 6-month, and 12-month). The performance of these models was rigorously validated using key metrics such as precision, accuracy, proportion incorrectly classified, over-all area under the curve (AUC), mean absolute error (MAE), and root mean square error (RMSE). Furthermore, the research extends its application to delineating drought vulnerability zones by establishing demarcations for high and very high drought vulnerability areas for each model and temporal scale. Results indicate that the south-western part of the middle Ganga plain falls under the highly drought-vulnerable zone, which averagely covers 40% of the study region. The core and buffer regions of drought vulnerability have also been identified. The south-western part of the study area is identified as the core region of drought. Ground verification of the drought-vulnerable area has been done by using soil moisture meter. Validation metrics show that the ensemble model of ANN and RF exhibits the highest accuracy across all temporal scales. This research's findings can be applied to improve drought preparedness and water resource management in the Middle Ganga Plain. By identifying high-risk drought zones and utilizing accurate prediction models, policymakers and farmers can implement targeted mitigation strategies. This approach could enhance agricultural resilience, protect livelihoods, and optimize water allocation in this vital agro-climatic region. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Meteorological influences on air pollution dynamics in pollution epicentre of National Capital Region, India
(Elsevier Ltd, 2025) Susanta Mahato; Sonali Kundu; Jan Cermak; Pawan Kumar Joshi
This study analyzes the temporal variations and source characteristics of air pollution in Delhi, examining the influence of meteorological conditions on pollutant concentrations. The goal is to provide insights for policymakers to develop effective emission reduction strategies and improve air quality. Innovative Trend Analysis (ITA) and Detrended Fluctuation Analysis (DFA) were used to identify long-term trends and fluctuations in pollutants such as PM10, PM2.5, NO2, SO2, CO, O3, and NH3 from 2018 to 2023. Continuous Wavelet Transformation (CWT) and Cross-Wavelet Transformation (XWT) were utilized to explore seasonal patterns and pollutant-meteorology interactions. Receptor modeling techniques, including non-parametric wind regression and conditional probability function analysis, were applied to identify major pollution sources. The study found that key emission sources were located to the west, south, and southwest of the monitoring site for most pollutants, with ozone precursors predominantly originating from the north. ITA and DFA revealed persistent long-range correlations in pollutant levels, driven by stable emission sources and seasonal meteorological effects. CWT analysis showed distinct periodic patterns in air quality, with worsening conditions during winter and summer. The research highlights the role of temperature inversions, low wind speeds, and regional pollutant transport in exacerbating pollution levels but emphasizes that human-driven emission sources remain the primary contributors to air quality deterioration. While meteorological factors influence pollution dispersion, they do not diminish the urgency of emission control measures. The findings support the development of targeted pollution control policies, including emission reduction from industrial, vehicular, and biomass burning sources. Advancing real-time air quality monitoring and integrating socio-economic considerations into air pollution management will enhance the effectiveness of interventions, aligning with Sustainable Development Goals (SDG 11 - urban sustainability and SDG 3 - public health). © 2025 Elsevier Ltd
Retraction notice to “Meteorological influences on air pollution dynamics in pollution epicentre of National Capital Region, India” [Chemosphere 377, May 2025, 144353] (Chemosphere (2025) 377, (S0045653525002954), (10.1016/j.chemosphere.2025.144353))
(Elsevier Ltd, 2025) Susanta Mahato; Sonali Kundu; Jan Cermak; Pawan Kumar Joshi
This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/policies/article-withdrawal). © 2025 Elsevier Ltd
Unravelling blue landscape fragmentation effects on ecosystem services in urban agglomerations
(Elsevier Ltd, 2024) Sonali Kundu; Narendra Kumar Rana; Susanta Mahato
The blue landscape (BL), which refers to the wetlands, rivers, lakes, and other bodies of water, is of immense importance for a sustainable environment. It provides a variety of ecosystem services, including carbon sequestration, water purification, soil fertility, and habitat for diverse flora and fauna. These services are crucial for sustaining the livelihoods of local communities and maintaining the health of the environment. In megacities like Kolkata, India, the BL is often subjected to fragmentation and degradation due to rapid urbanisation, industrialization, and other human activities. This has serious implications for ecosystem services and the overall health of the environment. This study is intended to value ecosystem service losses over the last 25 years and assess the influence of blue landscape fragmentation on ecosystem services in Kolkata, India. To accomplish this goal, satellite data was collected from 1994 to 2019 for both pre-monsoon and post-monsoon seasons. The results of the study showed that significant losses in ecosystem services were observed over this time period, primarily due to the decline of the East Kolkata Wetland and its conversion for settlement and agricultural purposes. Despite the existence of some policies aimed at protecting the wetland, more robust restrictions and remedial strategies are required to conserve and restore the blue landscape in Kolkata. This work adds to the corpus of research on the value of blue landscapes (BLs) and the environmental services they provide. The findings of this study highlight the need for increased attention to the conservation and management of BLs in India and provide a valuable foundation for future research and policy development. Furthermore, this research introduces the innovative concept of urban BLs as a nature-based framework for understanding urban landscape fragmentation, a novel contribution that fills a gap in the existing literature on this topic. © 2024 Elsevier Ltd
Wetland degradation and its impacts on livelihoods and sustainable development goals: An overview
(Elsevier B.V., 2024) Sonali Kundu; Barnali Kundu; Narendra Kumar Rana; Susanta Mahato
Wetlands, vital ecosystems that support 40 % of the world's species and serve as nature's water filters, are disappearing three times faster than forests. While global research extensively examines the increasing degradation of wetland health, there exists a significant research gap concerning its impact on livelihoods and the achievement of Sustainable Development Goals (SDGs). To address this gap, a systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, utilising data from 1270 database records and 350 research studies spanning 1990 to 2023. The study reveals alarming annual wetland health loss rates ranging from 0.02 % to 3.14 %, driven globally by built-up areas, agricultural expansion, climate change, and pollution. Notably, developing countries and those with lower development indices exhibit the highest rates of wetland health degradation, primarily attributed to agricultural and urban expansion, as well as pollution. The analysis establishes a negative correlation between wetland health degradation rates, driving factors, and key indicators such as the Sustainable Development Goal Index (SDGI) (r = −0.38), Environmental Performance Index (EPI) (r = −0.34), Income Classification (r = −0.42), and Human Development Index (HDI) (r = −0.38). The study emphasizes the imperative of improving economic and socio-ecological conditions to enhance conservation and restoration efforts in wetland areas, thereby contributing to the achievement of SDGs. The interconnectedness of wetland health with broader SDGs underscores the need for targeted interventions. Recommendations include prioritizing comprehensive strategies for environmental and societal well-being, urging policymakers and practitioners to consider the holistic implications of wetland health degradation in their decision-making processes. © 2024 Institution of Chemical Engineers