A diffusion model is considered to represent the time evolution of coastal profiles, over the time span of several years ahead. This is important in view of both, natural and anthropic activities, which affect and tend to modify the environment near the sea coastal lines, river’s estuaries, and harbors. The final aim is to make useful predictions for the depth profile evolution, basing on real available data, so that suitable actions might be planned. From the mathematical standpoint, the problem takes on the form of an inverse problem for a parabolic model equation, and thus the minimization of a suitable cost functional was accomplished to calibrate the model. An open source version of a genetic algorithm is used to identify the desired model parameters. To effectively reduce the overall computational cost, the code was parallelized. The computed and the measured depth profiles have been compared estimating the integrated relative error (IRE), using only ten years of measurements, each of them having been taken once a year. A regression analysis was carried out exploiting a machine learningalgorithm, provided by a support vector machine, to predict the evolution of the model parameters over one, two, and three years ahead. Predicted and measured depth profiles were compared in terms of IRE.

扩散模型被认为代表了未来几年时间跨度内海岸剖面的时间演变。考虑到自然和人为活动，这一点很重要，它们影响并倾向于改变沿海沿线、河流河口和港口附近的环境。最终目标是根据实际可用数据对深度剖面演变做出有用的预测，以便计划适当的行动。从数学的角度来看，该问题表现为抛物线模型方程的逆问题，因此实现了合适成本函数的最小化来校准模型。遗传算法的开源版本用于识别所需的模型参数。为了有效降低整体计算成本，代码被并行化。计算和测量的深度剖面已被比较，估计综合相对误差（IRE），仅使用十年的测量，每个测量每年进行一次。利用支持向量机提供的机器学习算法进行回归分析，以预测模型参数在未来一年、两年和三年内的演变。根据 IRE 比较预测和测量的深度剖面。