A Three-Phase Deep Learning Framework for Mine Reclamation: LSTM Prediction and DRL Control with Synthetic Data Validation Against African Soil Profiles

Abstract Mine reclamation across Sub-Saharan Africa faces a critical challenge: predicting and opti- mizing soil recovery without years of expensive field trials. This study introduces a three-phase deep learning framework that integrates Long Short-Term Memory (LSTM) networks for pH prediction with Deep Reinforcement Learning (DRL) for adaptive reclamation control.Phase 1 generates synthetic multi-sensor data (240,000 records across 100×100 grid, 6 features) validated against 1,839 real soil samples from the Africa Soil Information Service (AfSIS). The synthetic data appropriately captures mine-disturbed conditions (mean pH = 5.65 ± 0.86, significantly different from natural AfSIS soils, p < 0.001).Phase 2 implements an LSTM predictor achieving near-perfect accuracy (R² = 0.9976, RMSE = 0.0522, MAE = 0.0408). Rigorous cross-validation confirms no data leakage: temporal CV (RMSE = 0.0645 ± 0.0050), spatial block CV (mean difference = 0.021 pH), and stratified CV (RMSE = 0.0268 ± 0.0092).Phase 3 employs a DRL agent using Proximal Policy Optimization, achieving 11.6% pH improvement (from 4.98 baseline to 5.56). Valley floors showed greatest improvement (31.1%), while the Composite Ecological Score increased 107% (42.3 to 87.6).This framework offers a scalable, transferable approach for mine reclamation planning where field data are scarce, with all code and synthetic datasets publicly available.