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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Sliding Window-Based Machine Learning for Environmental Inspection Resource Allocation.

Qi Zhou^1,2, Shen Qu^1,2, Qianzi Wang^1,2

¹School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China.

Environmental Science & Technology

|October 23, 2023

Summary

This summary is machine-generated.

Machine learning models predict enterprise environmental inspection failures, improving resource allocation. This data-driven approach enhances regulatory effectiveness and promotes sustainable development.

Keywords:

Data-driven Environmental Management Inspection Allocation Machine Learning

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Area of Science:

Environmental Science
Data Science
Regulatory Science

Background:

Environmental regulation faces resource constraints, limiting inspection effectiveness.
Sustainable development requires efficient oversight of industrial compliance.
Predictive modeling can optimize resource allocation for environmental inspections.

Purpose of the Study:

To develop and evaluate machine learning models for predicting enterprise inspection failures.
To optimize resource allocation strategies for environmental regulators.
To enhance the effectiveness of environmental inspections using data-driven methods.

Main Methods:

Utilized four sliding window-based machine learning techniques.
Employed feature-engineered time-series data from 16,777 chemical enterprises (2010-2021).
Compared model performance against Long Short-Term Memory (LSTM) models, achieving ROC AUC > 0.83.

Main Results:

Machine learning models demonstrated high accuracy in predicting inspection failures.
Recent violation history significantly impacts future non-compliance.
Proposed risk-based resource allocation scenarios, with one increasing detection rates over 8-fold.

Conclusions:

Sliding window machine learning offers a viable alternative to deep learning for environmental regulation.
Data-driven insights enable optimized inspection resource allocation.
Enhanced regulatory effectiveness supports sustainable development goals.