Predictive Analytics for Long Term Environmental Remediation Cost Estimation

Abstract

This paper introduces a novel, cross-disciplinary predictive analytics framework
for estimating long-term environmental remediation costs, a domain traditionally
dominated by deterministic engineering models and expert judgment. The proposed
methodology uniquely integrates ecological succession modeling from theoretical
ecology with machine learning techniques, specifically a hybrid architecture combining Long Short-Term Memory (LSTM) networks and Gaussian Process Regression (GPR). This approach departs from conventional cost estimation by explicitly
modeling the non-linear, time-dependent feedback loops between biological recovery
processes, contaminant fate and transport, and evolving regulatory and economic
landscapes over decadal timescales. We formulate the problem not as a static financial projection but as a dynamic, high-dimensional spatiotemporal forecasting
challenge. The model is trained and validated on a newly compiled, multi-source
dataset spanning 45 historical remediation projects across North America and Europe, with timelines extending up to 30 years. Our results demonstrate that the
hybrid LSTM-GPR model significantly outperforms traditional linear regression
and standalone machine learning benchmarks, achieving a mean absolute percentage error (MAPE) of 18.7% on 20-year cost projections, compared to 42.3% for the
best conventional model. Crucially, the model provides not only point estimates
but also quantifiable, evolving uncertainty bounds that reflect the probabilistic
nature of ecological and regulatory change. The findings indicate that incorporating principles of ecological succession—such as threshold behaviors, resilience,
and alternative stable states—into the cost prediction pipeline captures critical cost
drivers previously omitted, leading to more robust and adaptive financial planning
for environmental stewardship. This work represents a fundamental shift from reactive accounting to proactive, systems-aware predictive analytics in environmental
finance.