Coppola Emery A, Rana Anthony J, Poulton Mary M, Szidarovszky Ferenc, Uhl Vincent W
NOAH, L.L.C., 610 Lawrence Road, Lawrenceville, NJ 08648-4208, USA.
Ground Water. 2005 Mar-Apr;43(2):231-41. doi: 10.1111/j.1745-6584.2005.0003.x.
Artificial neural networks (ANNs) were developed for accurately predicting potentiometric surface elevations (monitoring well water level elevations) in a semiconfined glacial sand and gravel aquifer under variable state, pumping extraction, and climate conditions. ANNs "learn" the system behavior of interest by processing representative data patterns through a mathematical structure analogous to the human brain. In this study, the ANNs used the initial water level measurements, production well extractions, and climate conditions to predict the final water level elevations 30 d into the future at two monitoring wells. A sensitivity analysis was conducted with the ANNs that quantified the importance of the various input predictor variables on final water level elevations. Unlike traditional physical-based models, ANNs do not require explicit characterization of the physical system and related physical data. Accordingly, ANN predictions were made on the basis of more easily quantifiable, measured variables, rather than physical model input parameters and conditions. This study demonstrates that ANNs can provide both excellent prediction capability and valuable sensitivity analyses, which can result in more appropriate ground water management strategies.
人工神经网络（ANNs）的开发目的是准确预测半承压冰川砂和砾石含水层在可变状态、抽水开采及气候条件下的测压面高程（监测井水位高程）。人工神经网络通过一个类似于人类大脑的数学结构处理代表性数据模式，“学习”感兴趣的系统行为。在本研究中，人工神经网络利用初始水位测量值、生产井抽水量及气候条件，预测两个监测井未来30天的最终水位高程。对人工神经网络进行了敏感性分析，量化了各种输入预测变量对最终水位高程的重要性。与传统的基于物理的模型不同，人工神经网络不需要对物理系统和相关物理数据进行明确表征。因此，人工神经网络的预测是基于更容易量化的测量变量，而非物理模型输入参数和条件。本研究表明，人工神经网络既能提供出色的预测能力，又能进行有价值的敏感性分析，从而制定更合适的地下水管理策略。
