Chau Kwok-wing
Department of Civil and Structural Engineering, Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong.
Mar Pollut Bull. 2006 Jul;52(7):726-33. doi: 10.1016/j.marpolbul.2006.04.003. Epub 2006 Apr 22.
With the development of computing technology, numerical models are often employed to simulate flow and water quality processes in coastal environments. However, the emphasis has conventionally been placed on algorithmic procedures to solve specific problems. These numerical models, being insufficiently user-friendly, lack knowledge transfers in model interpretation. This results in significant constraints on model uses and large gaps between model developers and practitioners. It is a difficult task for novice application users to select an appropriate numerical model. It is desirable to incorporate the existing heuristic knowledge about model manipulation and to furnish intelligent manipulation of calibration parameters. The advancement in artificial intelligence (AI) during the past decade rendered it possible to integrate the technologies into numerical modelling systems in order to bridge the gaps. The objective of this paper is to review the current state-of-the-art of the integration of AI into water quality modelling. Algorithms and methods studied include knowledge-based system, genetic algorithm, artificial neural network, and fuzzy inference system. These techniques can contribute to the integrated model in different aspects and may not be mutually exclusive to one another. Some future directions for further development and their potentials are explored and presented.
随着计算技术的发展，数值模型常被用于模拟沿海环境中的水流和水质过程。然而，传统上重点一直放在解决特定问题的算法程序上。这些数值模型对用户不够友好，在模型解释方面缺乏知识传递。这导致模型使用受到重大限制，模型开发者和从业者之间存在很大差距。对于新手应用用户来说，选择合适的数值模型是一项艰巨的任务。将现有的关于模型操作的启发式知识纳入并提供校准参数的智能操作是很有必要的。过去十年人工智能（AI）的进步使得将这些技术集成到数值建模系统中以弥合差距成为可能。本文的目的是回顾人工智能与水质建模集成的当前最新技术水平。所研究的算法和方法包括基于知识的系统、遗传算法、人工神经网络和模糊推理系统。这些技术可以在不同方面为集成模型做出贡献，并且可能并非相互排斥。探索并呈现了一些进一步发展的未来方向及其潜力。
