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.

中文翻译：

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空间有针对性的植树造林，以尽量减少流域泥沙流失：一种考虑空间相互作用的有效爬山方法

基于土壤侵蚀和沉积物输送过程，CAMF（基于元胞自动机的最小化流量启发式算法）选择植树造林地点，以最大限度地减少流域出口处的沉积物流入。CAMF 使用流域的栅格表示和最陡的爬山优化启发式，保护空间交互。对于大型数据集，其执行时间可能会非常长。并行化可在 28 个内核上实现 20 至 24 倍的加速。我们提出了优化方法的变体，以减少迭代的次数和成本。我们提出了一种针对这些变体的元参数的调整算法。两个对比集水区获得的结果表明，加速使成本降低了 100 以上，而对绿化细胞和沉积物减少幅度的影响可以忽略不计。结果表明，更高水平的空间交互对结果的准确性和/或执行时间的影响更大。