Modelling human health vulnerability using different machine learning algorithms in stone quarrying and crushing areas of Dwarka river Basin, Eastern India

Mandal, Indrajit; Pal, Swades

doi:10.1016/j.asr.2020.05.032

Cited by 17 publications

(8 citation statements)

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“…Camejo and others said that from multiple perspectives of current social information data processing and traditional information data processing algorithms, some large-scale data are no longer applicable. In today's society where data flow and information flow are exploding, in-depth data mining has gradually developed into a new trend [18]. Khan and others have found through surveys that the transformation of enterprises and the progress of the industry require the improvement of machine learning algorithms to easily process huge amounts of information and data to achieve social development; thereby, the huge information data can be conveniently processed to realize the development of society [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of Printing and Dyeing Energy Consumption Based on Multimedia Machine Learning Algorithm

Zhang¹,

2022

Security and Communication Networks

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In order to solve the problem with the expansion of the industrial scale, the contradiction between energy resources and development of printing and dyeing enterprises must implement refined management and optimize the allocation of resources such as production technology, energy consumption types, and metering instruments. As a printing and dyeing textile industry with high energy consumption and high pollution, energy conservation and emission reduction have become difficult problem to be solved in this industry. The premise of optimizing resource allocation is to have an objective and scientific evaluation of the current energy resource allocation of printing and dyeing enterprises. In view of this, through the investigation of printing and dyeing enterprises, this paper puts forward the index system of enterprise energy consumption optimization evaluation. Based on the application of data warehouse and combined with historical data, a new energy consumption optimization evaluation method is proposed. This survey has basically understood the current situation of energy and water resources management of a printing and dyeing enterprise and pointed out the direction for the development of enterprise energy optimization project in the next step. By means of multimedia informatization, with the help of Internet of Things sensing technology, building a new generation of energy management system, improving the refined management of energy measurement, and improving the energy assessment system, enterprises can achieve significant economic and social benefits in terms of energy conservation and emission reduction. On this basis, a low-power scheduling strategy for typical data center applications is designed and implemented. The algorithm uses the input data of different calculation parts, performs matching to determine the redundant part of the calculation process, and schedules the algorithm. Experimental data show that the mean square error of the limit tree is 0.0004248 and the mean square error of the decision tree is 0.01581; through calculation, the algorithm can achieve 23% and 17% energy saving.

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Section: Literature Reviewmentioning

confidence: 99%

Optimization of Printing and Dyeing Energy Consumption Based on Multimedia Machine Learning Algorithm

Zhang¹,

2022

Security and Communication Networks

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“…With the integration of multiple dimensions, the vulnerability perspective has been extended from disaster vulnerability to multiple fields, such as social, ecological, and economic vulnerability (Weißhuhn et al, 2018 ). Further, it has been suggested that public health relates to vulnerability (Mandal & Pal, 2020 ). As scholars demonstrate, vulnerable individuals, cities, and countries can be strongly impacted by health risks (Razavi et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

An Integrated Analysis of Social, Economic, and Environmental Indicators’ Effects on Public Health and Health Inequality Globally: From the Perspective of Vulnerability

Yang

Geng

2022

Soc Indic Res

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Public health and health inequality have been widely researched as they are essential for human development and social justice. Although factors influencing public health and health inequality have been explored, an integrated and comprehensive analysis of social, economic, and environmental indicators’ effects on public health and health inequality globally is yet to be conducted. The current study addresses this gap by using a theoretical framework that integrates these three dimensions, examining their effects on public health and health inequality from the perspective of vulnerability. Considering the spatial heterogeneity across countries, spatial econometric models and geographically weighted regressions were conducted for the examination of these effects. Our findings reveal the social indicators of urbanisation ratio, social education level, and social governance capacity had positive effects on public health promotion and health inequality elimination globally. Besides these, environmental risk and economic capacity had relatively low impacts on health inequality. Further, the geographically weighted regression results indicate that vulnerability’s effects on public health and health inequality varied significantly across countries. This integrated analysis provides national policymakers with a new perspective of vulnerability for public health promotion and health inequality reduction.

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“…Random forest is a well-known machine learning algorithm for both regression and classification tasks (Breiman, 2001;Naghibi et al 2017;Mandal and Pal, 2020). For regression and classification of the training datasets, researchers will attempt to develop a decision tree (DT) for class-wise output extraction and to acquire the dependent variable (Sachdeva and Kumar, 2020).…”

Section: Random Forest (Rf)mentioning

confidence: 99%

“…Presently, these models are not used frequently because they do not solve multi-criteria decision problems. Therefore, numerous machine learning approaches offer more effective and efficient potential decisions using geographical information systems (GIS) and remote sensing (RS) data (Kanevski et al 2009;Micheletti et al 2013). The major strength of machine learning is its capability to handle data with high dimensionality and to map those with complex classification issues.…”

Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Groundwater Potentiality Mapping Using Machine Learning Algorithms

Saha¹,

Gayen²,

Mukherjee³

et al. 2021

Preprint

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Machine learning techniques offer powerful tools for the assessment and management of groundwater resources. Here, we evaluated the groundwater potential maps (GWPMs) in Md. Bazar Block of Birbhum District, India using four GIS-based machine-learning algorithms (MLA) such as predictive neural network (PNN), decision tree (DT), Naïve Bayes classifier (NBC), and random forest (RF). We used a database of 85 dug wells and one piezometer location identified using extensive field study, and employed 12 influencing factors (elevation, slope, drainage density (DD), topographical wetness index, geomorphology, lineament density, rainfall, geology, pond density, land use/land cover (LULC), geology, and soil texture) for evaluation through GIS. The 85 dug wells and 1 piezometer locations were sub-divided into two classes: 70:30 for training and model validation. The DT, RF, PNN, and NBC MLAs were implemented to analyse the relationship between the dug well locations and groundwater influencing factors to generate GWPMs. The results predict excellent groundwater potential areas (GPA) DT RF of 17.38%, 14.69%, 20.43%, and 13.97% of the study area, respectively. The prediction accuracy of each GWPM was determined using a receiver operating characteristic (ROC) curve. Using the 30% data sets (validation data), accuracies of 80.1%, 78.30%, 75.20%, and 69.2% were obtained for the PNN, RF, DT, and NBC models, respectively. The ROC values show that the four implemented models provide satisfactory and suitable results for GWP mapping in this region. In addition, the well-known mean decrease Gini (MDG) from the RF MLA was implemented to determine the relative importance of the variables for groundwater potentiality assessment. The MDG revealed that drainage density, lineament density, geomorphology, pond density, elevation, and stream junction frequency were the most useful determinants of GWPM. Our approach to delineate the GWPM can aid in the effective planning and management of groundwater resources in this region.

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Modelling human health vulnerability using different machine learning algorithms in stone quarrying and crushing areas of Dwarka river Basin, Eastern India

Cited by 17 publications

References 100 publications

Optimization of Printing and Dyeing Energy Consumption Based on Multimedia Machine Learning Algorithm

Optimization of Printing and Dyeing Energy Consumption Based on Multimedia Machine Learning Algorithm

An Integrated Analysis of Social, Economic, and Environmental Indicators’ Effects on Public Health and Health Inequality Globally: From the Perspective of Vulnerability

Spatial Prediction of Groundwater Potentiality Mapping Using Machine Learning Algorithms

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