Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping

2021 ◽  
Author(s):  
Duong Hai Ha ◽  
Phong Tung Nguyen ◽  
Romulus Costache ◽  
Nadhir Al-Ansari ◽  
Tran Van Phong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Groundwater Potential ◽  
Quadratic Discriminant Analysis ◽  
Learning Models ◽  
Ensemble Machine Learning ◽  
Machine Learning Models

scholarly journals Data-Driven Approach for Predicting and Explaining the Risk of Long-Term Unemployment

2020 ◽  
Vol 214 ◽  
pp. 01023
Author(s):  
Linan (Frank) Zhao
Keyword(s):  
Machine Learning ◽  
Age Groups ◽  
Learning Models ◽  
Public Authorities ◽  
Ensemble Machine Learning ◽  
European Public ◽  
Data Driven Approach ◽  
Using Data ◽  
Machine Learning Models

Long-term unemployment has significant societal impact and is of particular concerns for policymakers with regard to economic growth and public finances. This paper constructs advanced ensemble machine learning models to predict citizens’ risks of becoming long-term unemployed using data collected from European public authorities for employment service. The proposed model achieves 81.2% accuracy on identifying citizens with high risks of long-term unemployment. This paper also examines how to dissect black-box machine learning models by offering explanations at both a local and global level using SHAP, a state-of-the-art model-agnostic approach to explain factors that contribute to long-term unemployment. Lastly, this paper addresses an under-explored question when applying machine learning in the public domain, that is, the inherent bias in model predictions. The results show that popular models such as gradient boosted trees may produce unfair predictions against senior age groups and immigrants. Overall, this paper sheds light on the recent increasing shift for governments to adopt machine learning models to profile and prioritize employment resources to reduce the detrimental effects of long-term unemployment and improve public welfare.

scholarly journals Sex estimation in cranial remains: A comparison of machine learning and discriminant analysis in Italian populations

2020 ◽  
Author(s):  
A Pozzi ◽  
C Raffone ◽  
MG Belcastro ◽  
TL Camilleri-Carter
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Foramen Magnum ◽  
Sex Estimation ◽  
Learning Models ◽  
Linear Discriminant ◽  
Machine Learning Methods ◽  
Cranial Measurements ◽  
Machine Learning Models

ABSTRACTObjectivesUsing cranial measurements in two Italian populations, we compare machine learning methods to the more traditional method of linear discriminant analysis in estimating sex. We use crania in sex estimation because it is useful especially when remains are fragmented or displaced, and the cranium may be the only remains found.Materials and MethodsUsing the machine learning methods of decision tree learning, support-vector machines, k-nearest neighbor algorithm, and ensemble methods we estimate the sex of two populations: Samples from Bologna and samples from the island of Sardinia. We used two datasets, one containing 17 cranial measurements, and one measuring the foramen magnum.Results and DiscussionOur results indicate that machine learning models produce similar results to linear discriminant analysis, but in some cases machine learning produces more consistent accuracy between the sexes. Our study shows that sex can be accurately predicted (> 80%) in Italian populations using the cranial measurements we gathered, except for the foramen magnum, which shows a level of accuracy of ∼70% accurate which is on par with previous geometric morphometrics studies using crania in sex estimation. We also find that our trained machine learning models produce population-specific results; we see that Italian crania are sexually dimorphic, but the features that are important to this dimorphism differ between the populations.

scholarly journals Medium-term load forecasting in isolated power systems based on ensemble machine learning models

2022 ◽  
Vol 8 ◽  
pp. 612-618
Author(s):  
Pavel Matrenin ◽  
Murodbek Safaraliev ◽  
Stepan Dmitriev ◽  
Sergey Kokin ◽  
Anvari Ghulomzoda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Load Forecasting ◽  
Learning Models ◽  
Medium Term ◽  
Ensemble Machine Learning ◽  
Machine Learning Models

scholarly journals IoT-Enabled Flood Severity Prediction via Ensemble Machine Learning Models

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 70375-70386 ◽  
Author(s):  
Mohammed Khalaf ◽  
Haya Alaskar ◽  
Abir Jaafar Hussain ◽  
Thar Baker ◽  
Zakaria Maamar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Ensemble Machine Learning ◽  
Severity Prediction ◽  
Machine Learning Models

scholarly journals Characterizing Groundwater Potential Using GIS-Based Machine Learning Model in Chihe River Basin, China

2021 ◽  
Author(s):  
Dejian Wang ◽  
Jiazhong Qian ◽  
Lei Ma ◽  
Weidong Zhao ◽  
Di Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Resources ◽  
River Basin ◽  
Environmental Variables ◽  
Groundwater Potential ◽  
High Potential ◽  
Slope Aspect ◽  
Learning Models ◽  
Topographic Wetness Index ◽  
Machine Learning Models

Abstract Mapping of groundwater potential over space, built by synergizing environmental variables and machine learning models, was of great significance for regional water resources management. Taking the Chihe River basin in Anhui province as an example, thirteen influence factors were used to predict the spatial distribution of groundwater, including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), drainage density, distance to rivers, distance to faults, lithology, soil type, land use, and normalized difference vegetation index (NDVI). The potential of groundwater resource in this region was predicted using GIS-based machine learning models, including logistic regression (LR), deep neural networks (DNN), and random forest (RF) model. Then, the accuracy of prediction results was evaluated by calculating the RMSE, MAE and R evaluation index. The results show that there is no collinearity among the 13 environmental impact factors, which can provide corresponding environmental variables for the evaluation of regional groundwater potential. Machine learning models show that groundwater potential is concentrated in moderate to high potential areas. Among them, the moderate to the high potential of this area accounted for 81.14% in the LR model, 90.36% and 87.55% in the DNN model and the RF model, respectively. According to the result of these evaluation indexes, the three models all have high prediction accuracy, among which the LR model performs more prominently. The good prediction capabilities of these machine learning technologies can provide a reliable scientific basis for spatial prediction of groundwater potential and management of water resources.

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