Forecasting basal area increment in forest ecosystems using deep learning: A multi-species analysis in the Himalayas

This study addresses the task of forecasting Basal Area Increment trends in forest ecosystems, which is essential for conservation and biodiversity management, particularly in the context of climate change. Traditional forecasting techniques, such as Linear Mixed Models, Random Forest and standard Artificial Neural Networks, often fail to account for the time-dependent nature of tree growth and utilize simple architectures. To overcome these limitations, we introduce the use of two different Deep Learning models: the Long Short-Term Memory network and the Temporal Convolutional Neural Network, which capture the temporal dependencies of growth by incorporating lagged Basal Area Increment values. Our methodology includes rigorous hyperparameter tuning to optimize the Deep Learning models’ architecture. We evaluate the models’ performance across 15 species in the Himalayan region, individually and collectively, using temperature and precipitation data as predictors. The Deep Learning model significantly outperforms state-of-the-art techniques, achieving the lowest Root Mean Squared Error (7.407 for LSTM and 6.202 for TCNN), highest R 2 (0.495 for LSTM and 0.585 for TCNN) and lowest Mean Absolute Percentage Error values (37.653 for LSTM and 34.296 for TCNN). These findings highlight the potential of Deep Learning networks to provide accurate and reliable Basal Area Increment forecasts, offering valuable insights for forest management and conservation efforts in the face of ongoing climate change. • DL showed potential for BAI forecasting. • Larger datasets improve the performance of DL models. • ANN, SVM and LR failed to predict BAI accurately. • GBT models reported temporal shifts in the merged dataset. • DL models perform best in RMSE, R 2 and MAPE, which proves to be a robust solution for BAI forecasting.