Declining groundwater levels due to the absence of a planning system makes aquifers vulnerable to subsidence. This paper investigates possible hotspots in terms of Subsidence Vulnerability Indices (SVI) by applying the ALPRIFT framework, introduced recently by the authors by mirroring the procedure for the DRASTIC framework. ALPRIFT is suitable to cases, where data is sparse, and is the acronym of seven data layers to be presented in due course. It is a scoring technique, in which each data layer bears an aspect of land subsidence and is prescribed with rates to account for local variability, and with prescribed weights to account for relative significance of the data layer. The inherent subjectivity in prescribed weights is treated in this paper by learning their values from site-specific data by the strategy of using artificial intelligence to learn from multiple models (AIMM). The strategy has two levels: (i) at Level 1, three fuzzy models are used to learn weight values from the local data and from observed target data, and (ii) at Level 2, genetic expression algorithm (GEP) is used to learn further, in which the outputs of the models at Level 1 are reused as its inputs and observed data as its target values. The results show that (i) the Nash-Sutcliff Efficiency (NSE) coefficient for ALPRIFT with measured land subsidence values is approx. 0.21; (ii) NSE is improved to 0.88 by learning the weights at Level 1 using fuzzy logic, and (iii) NSE is further improved to 0.94 by further learning at Level 2 using GEP.

由于缺乏规划系统而导致地下水位下降，使含水层容易沉陷。本文通过应用ALPRIFT框架，根据沉陷漏洞指数（SVI）调查了可能的热点，作者最近通过镜像DRASTIC框架的过程介绍了ALPRIFT框架。ALPRIFT适用于数据稀疏的情况，并且是在适当时候提供的七个数据层的缩写。这是一种计分技术，其中每个数据层都具有地面沉降的方面，并规定了速率以考虑局部变化，并规定了权重以考虑数据层的相对重要性。通过使用人工智能从多个模型（AIMM）学习的策略，通过从特定地点的数据中学习它们的值，来处理规定权重中的固有主观性。该策略分为两个级别：（i）在级别1，使用三个模糊模型从本地数据和观察到的目标数据中学习权重值，并且（ii）在级别2，使用遗传表达算法（GEP）来学习此外，其中将级别1的模型的输出重新用作其输入，并将观察到的数据用作其目标值。结果表明：（i）ALPRIFT的Nash-Sutcliff效率（NSE）系数与测得的地面沉降值近似。0.21；（ii）通过使用模糊逻辑学习第1级的权重将NSE提高到0.88，并且（iii）通过使用GEP在第2级的进一步学习将NSE进一步提高到0.94。