Certain degree of deformation is natural while dam operates and evolves. Due to the impact of internal and external environment, dam deformation is highly nonlinear by nature. For dam safety, it is of great significance to analyze timely deformation monitoring data and be able to predict reliably deformation. A comprehensive review of existing deformation prediction models reveals two issues that deserves further attention: (1) each environmental influencing factor contributes differently to deformation, and (2) deformation lags behind environmental factors (e.g., water level and air temperature). In response, this study presents a combination deformation prediction model considering both quantitative evaluation of influencing factors and hysteresis correction in order to further improve estimation accuracy. In this study, the complex relationship in deformation prediction is effectively captured through support vector machine (SVM) modeling. Furthermore, a modified fruit fly optimization algorithm (MFOA) is presented for SVM hyper-parameter optimization. Also, a synthetic evaluation method and a hysteresis quantification algorithm are introduced to further enhance the MFOA-SVM-based model in regards to contribution quantification and phase correction respectively. The accuracy and validity of the proposed model is evaluated in a concrete dam case, where its performance is compared with other existing models. The simulated results indicated that the proposed nonlinear MFOA-SVM model considering both quantitative evaluation and hysteresis correction, abbreviated as SEV-MFOA-SVM, is more accurate and robust than conventional models. This novel model also provides an alternative method for predicting and analyzing dam deformation and evolution behavior of other similar hydraulic structures.

大坝运行和演化时，一定程度的变形是自然的。由于内部和外部环境的影响，大坝变形本质上是高度非线性的。对于大坝安全而言，及时分析变形监测数据并能够可靠地预测变形具有重要意义。对现有变形预测模型的全面回顾揭示了两个值得进一步关注的问题：（1）每个环境影响因素对变形的贡献不同，（2）变形滞后于环境因素（例如水位和气温）。作为回应，本研究提出了一种组合变形预测模型，该模型同时考虑了影响因素的定量评估和滞后校正，以进一步提高估计精度。在这个研究中，通过支持向量机（SVM）建模可以有效地捕获变形预测中的复杂关系。此外，提出了一种改进的果蝇优化算法（MFOA），用于SVM超参数优化。此外，引入了一种综合评估方法和一个磁滞量化算法，以进一步增强基于MFOA-SVM的模型，分别在贡献量化和相位校正方面。在混凝土大坝案例中评估了所提出模型的准确性和有效性，并将其性能与其他现有模型进行了比较。仿真结果表明，所提出的同时考虑了定量评估和滞后校正的非线性MFOA-SVM模型比常规模型更准确，更可靠。