Lake water-level fluctuation is a complex and dynamic process, characterized by high stochasticity and nonlinearity, and difficult to model and forecast. In recent years, applications of machine learning (ML) models have yielded substantial progress in forecasting lake water-level fluctuations. This paper presents a comprehensive review of the applications of ML models for modeling water-level dynamics in lakes. Among the many existing ML models, seven popular ML model types are reviewed: (1) artificial neural network (ANN); (2) support vector machine (SVM); (3) artificial neuro-fuzzy inference system (ANFIS); (4) hybrid models, such as hybrid wavelet-artificial neural network (WA-ANN) model, hybrid wavelet-artificial neuro-fuzzy inference system (WA-ANFIS) model, and hybrid wavelet-support vector machine (WA-SVM) model; (5) evolutionary models, such as gene expression programming (GEP) and genetic programming (GP); (6) extreme learning machine (ELM); and (7) deep learning (DL). Model inputs, data split, model performance criteria, and model inter-comparison as well as the associated issues are discussed. The advantages and limitations of the established ML models are also discussed. Some specific directions for future research are also offered. This review provides a new vision for hydrologists and water resources planners for sustainable management of lakes.

中文翻译：

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使用机器学习模型的湖泊水位波动预测：系统综述。

湖泊水位涨落是一个复杂而动态的过程，具有高度的随机性和非线性，难以建模和预测。近年来，机器学习（ML）模型的应用在预测湖泊水位波动方面取得了实质性进展。本文对ML模型在湖泊水位动力学建模中的应用进行了全面综述。在许多现有的ML模型中，对7种流行的ML模型类型进行了回顾：（1）人工神经网络（ANN）；（2）支持向量机（SVM）；（3）人工神经模糊推理系统（ANFIS）；（4）混合模型，例如混合小波-人工神经网络（WA-ANN）模型，混合小波-人工神经模糊推理系统（WA-ANFIS）模型和混合小波-支持向量机（WA-SVM）模型; （5）进化模型，例如基因表达编程（GEP）和遗传编程（GP）；（6）极限学习机（ELM）；（7）深度学习（DL）。讨论了模型输入，数据拆分，模型性能标准，模型之间的比较以及相关问题。还讨论了已建立的ML模型的优缺点。还提供了一些未来研究的具体方向。这次审查为水文学家和水资源规划者提供了湖泊可持续管理的新视野。还讨论了已建立的ML模型的优缺点。还提供了一些未来研究的具体方向。这次审查为水文学家和水资源规划者提供了湖泊可持续管理的新视野。还讨论了已建立的ML模型的优缺点。还提供了一些未来研究的具体方向。这次审查为水文学家和水资源规划者提供了湖泊可持续管理的新视野。