Climate change vulnerability, and health of women and children: Evidence from India using district level data

Mahapatra, Bidhubhusan; Chaudhuri, Trisha; Saggurti, Niranjan

doi:10.1002/ijgo.14515

Cited by 5 publications

(1 citation statement)

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“…Research on the impact of climate change on the region’s severe rainfalls has shown that these events are non-stationarous (Yilmaz 2015 ; Yilmaz 2017 ). Understanding local climate vulnerabilities is crucial because of the influence of climate change on a variety of factors, including agricultural pests (Kutywayo et al 2013 ), erosion and land cover changes (Coşgun 2017 ), and health consequences (Mahapatra et al 2022 ). These effects have been examined in numerous districts worldwide.…”

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confidence: 99%

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration

Akiner,

Ghasri

2024

Environ Sci Pollut Res

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Accurately predicting potential evapotranspiration (PET) is crucial in water resource management, agricultural planning, and climate change studies. This research aims to investigate the performance of two machine learning methods, the adaptive network-based fuzzy inference system (ANFIS) and the deep belief network (DBN), in forecasting PET, as well as to explore the potential of hybridizing the ANFIS approach with the Snake Optimizer (ANFIS-SO) algorithm. The study utilized a comprehensive dataset spanning the period from 1983 to 2020. The ANFIS, ANFIS-SO, and DBN models were developed, and their performances were evaluated using statistical metrics, including R2, $${R}_{adj}^2$$ R adj 2 , NSE, WI, STD, and RMSE. The results showcase the exceptional performance of the DBN model, which achieved R2 and $${R}_{adj}^2$$ R adj 2 values of 0.99 and NSE and WI scores of 0.99 across the nine stations analyzed. In contrast, the standard ANFIS method exhibited relatively weaker performance, with R2 and $${R}_{adj}^2$$ R adj 2 values ranging from 0.52 to 0.88. However, the ANFIS-SO approach demonstrated a substantial improvement, with R2 and $${R}_{adj}^2$$ R adj 2 values ranging from 0.94 to 0.99, suggesting the value of optimization techniques in enhancing the model’s capabilities. The Taylor diagram and violin plots with box plots further corroborated the comparative analysis, highlighting the DBN model’s superior ability to closely match the observed standard deviation and correlation and its consistent and low-variance predictions. The ANFIS-SO method also exhibited enhanced performance in these visual representations, with an RMSE of 0.86, compared to 0.95 for the standard ANFIS. The insights gained from this study can inform the selection of the most appropriate modeling technique, whether it be the high-precision DBN, the flexible ANFIS, or the optimized ANFIS-SO approach, based on the specific requirements and constraints of the application.

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Section: Introductionmentioning

confidence: 99%

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration

Akiner,

Ghasri

2024

Environ Sci Pollut Res

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Climate change and human health: estimating district-level health vulnerabilities in the Indian context

Chaudhry,

Mukhopadhyay

2023

Climatic Change

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Evidence on the health impact of climate change has been extensively documented in recent scholarly literature. In order to mitigate the adverse health effects induced by climate change, the need for conducting vulnerability assessments has been emphasized. A higher vulnerability to climate change is often linked with substantial risks to human lives and built environment. Despite the potential of vulnerability assessments in alleviating risks posed by climate change, only a limited amount of scholarly work in this domain has been conducted in the Indian context. The present research addresses this lacuna and contributes to the limited scholarship on climate change and health vulnerability assessments in India. Drawing on the vulnerability assessment framework introduced by the fourth assessment report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), this paper estimates district-level health vulnerabilities caused by climate change using multi-dimensional indices. The indices are multi-dimensional since they integrate 50 district-level indicators from 8 data sources for all 640 Census 2011 districts. The statistical technique of Principal Component Analysis (PCA) has been used for integrating the indicators. The findings of this paper indicate that nearly 56 percent of India's population in 344 districts is highly vulnerable to the health impact of climate change. The results also suggest that Uttar Pradesh,

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Upholding the Human Rights and Well-Being of Refugee Children Through Effective Clinical Care

Fortuna,

Porche

2024

Child and Adolescent Psychiatric Clinics of North America

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Climate change vulnerability, and health of women and children: Evidence from India using district level data

Cited by 5 publications

References 43 publications

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration

Climate change and human health: estimating district-level health vulnerabilities in the Indian context

Upholding the Human Rights and Well-Being of Refugee Children Through Effective Clinical Care

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