Spatially targeted afforestation to minimize sediment loss from a catchment: An efficient hill climbing method considering spatial interaction.

Based on soil erosion and sediment transport processes, CAMF (Cellular Automata-based heuristic for Minimizing Flow) selects sites for afforestation to minimize sediment influx at a catchment's outlet. CAMF uses a raster representation of the catchment and a steepest ascent hill-climbing optimization heuristic, safeguarding spatial interaction. Its execution time can be prohibitively long for large data-sets. Parallelization results in a speedup of 20 to 24 on 28 cores. We present variants of the optimization method to reduce the number and cost of the iterations. We present a tuning algorithm for the meta-parameters of these variants. The results obtained for two contrasting catchments illustrate that the accelerations reduce the cost by a factor larger than 100, with negligible effect on the afforested cells and magnitude of the sediment reduction. The results indicate that higher levels of spatial interaction have a stronger impact on the accuracy of the results and/or the execution time. • An efficient method to select sites for afforestation to minimize sediment loss. • Modified steepest ascent hill climbing method reduces runtime with minor quality loss. • A parallel implementation achieves nearly optimal speedup on multi-core processors. • Application to two river catchments shows the impact of spatial interaction. [ABSTRACT FROM AUTHOR]