Abstract
Each country needs to monitor progress on their Sustainable Development Goals (SDGs) to develop strategies that meet the expectations of the United Nations. Data envelope analysis (DEA) can help identify best practices for SDGs by setting goals to compete against. Automated machine learning (AutoML) simplifies machine learning for researchers who need less time and manpower to predict future situations. This work introduces an integrative method that integrates DEA and AutoML to assess and predict performance in SDGs. There are two experiments with different data properties in their interval and correlation to demonstrate the approach. Three prediction targets are set to measure performance in the regression, classification, and multi-target regression algorithms. The back-propagation neural network (BPNN) is used to validate the outputs of the AutoML. As a result, AutoML can outperform BPNN for regression and classification prediction problems. Low standard deviation (SD) data result in poor prediction performance for the BPNN, but does not have a significant impact on AutoML. Highly correlated data result in a higher accuracy, but does not significantly affect the R-squared values between the actual and predicted values. This integrative approach can accurately predict the projected outputs, which can be used as national goals to transform an inefficient country into an efficient country.
Highlights
Sustainable Development Goals (SDGs) are a global plan for developing sustainability in order to eradicate poverty while preserving the environment and quality of life of all living things around the world without leaving anyone behind
The integrative approach consists of four main steps, as shown in Figure 1: 1. implementing a stratification Data envelope analysis (DEA) to evaluate the efficiency score (ES) and efficiency tier (ET), and to examine the projected output (PO) that will turn an inefficient decision-making unit (DMU) into an efficient DMU; 2. applying Automated machine learning (AutoML) to predict the three outcomes of the DEA using classification algorithms to determine the ET and regression algorithms to predict the ES and PO; 3. using a back-propagation neural network (BPNN) to produce a series of the same results to validate the AutoML outputs by comparing the rates of precision and accuracy along with the number of DMUs within an acceptable percentage error (PRED); Sustainability 2020, 12, 10124
The BPNN could predict greater than 50% accuracy in SDG7, AutoML could predict greater than 90% accuracy
Summary
Sustainable Development Goals (SDGs) are a global plan for developing sustainability in order to eradicate poverty while preserving the environment and quality of life of all living things around the world without leaving anyone behind. According to the 2019 Sustainable Development Report, none of the countries is currently pursuing a path to achieve the 17 goals [1]. Each country needs to monitor its progress in order to develop and promote strategies that meet the expectations of the SDGs [2]. Benchmarking, by setting goals to compete with, can help identify best practices and apply them reliably to improve a country’s performance towards reaching their SDGs. Data envelope analysis (DEA) is a benchmarking technique in which the optimal combinations of effort (input) and performance (output) result from a linear optimization problem. Every country must set future goals in order to achieve their SDGs; DEA lacks predictive capability [3,4,5,6]. Some studies have tried to integrate DEA with other methods, such as machine learning, to predict future situations
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