Year: 2025
Type: article
Abstract: Purpose The goal of this study is to predict how well five countries the US, Saudi Arabia, China, Egypt, and Sweden will do in terms of Sustainable Development Goal 2 (SDG 2), particularly the hunger index scores, between 2025 and 2030. Methods Historical agricultural, nutritional, and socioeconomic data from 2000 to 2022 were analyses and temporal patterns were extracted using a one-dimensional Convolutional Neural Network (1D-CNN). To guarantee precise and believable predictions, the model was trained and verified using historical data. To represent realistic development trajectories toward SDG 2 targets, forecasts were limited to a range of 0 to 100. Results By identifying minor temporal trends in line with patterns of world development, the 1D-CNN model showed great accuracy in forecasting changes in hunger index scores. The predictions point to possible advancements in the nations under study in terms of lowering hunger and enhancing food security. Conclusions Policymakers, international organizations, and sustainability advocates may all benefit from the insightful data that the suggested forecasting technique offers. These forecasts encourage more focused initiatives and efficient use of resources, which will eventually speed up efforts to meet SDG 2 (Zero Hunger).
Language: English
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,353
OpenAlex ID: W4413093562