Bathymetry is a knowledge of water depth calculation, which is of great importance in many environmental management applications. The objective of this study is to improve the accuracy of the traditional ratio model as a widely used experimental bathymetry method. Therefore, firstly hybrid methods were proposed, which combined principal component analysis (PCA) and image fusion methods, to obtain more informative inputs for the Nayband bay bathymetry mapping under conditions of high turbidity. The results showed that the proposed hybrid bathymetry methods highly improved the accuracy of the depth maps. Then, two new algorithms, namely HybF_PSO and HybF_GA, have been introduced based on combining the proposed hybrid methods and the particle swarm optimization (PSO) or the genetic algorithm (GA) optimization methods. PSO and GA optimization methods were utilized to calculate the constant parameters of the depth model optimally. Compared to the traditional ratio model, the HybF_GA algorithm improved the bathymetry accuracy of depths shallower than − 2 m from 2.93 to 2.53, depths between − 2 and − 4 m from 3.2 to 1.56, depths between − 4 and − 8 m from 2.4 to 1.88, and areas deeper that − 8 m from 5.24 to 2.93. The HybF_PSO algorithm improved the accuracy of mapping areas deeper than − 8 m even more than the HybF_GA algorithm. Compared to the traditional ratio model, the HybF_PSO algorithm also highly improved the bathymetry accuracy in all the depth classes. Therefore, it can be concluded that the proposed bathymetry algorithms are very applicable and helpful.

测深是水深计算的知识，这在许多环境管理应用中非常重要。本研究的目的是提高传统比率模型作为一种广泛使用的实验测深方法的准确性。因此，首先提出了混合方法，将主成分分析（PCA）和图像融合方法相结合，为高浊度条件下的Nayband海湾测深测绘获得更多信息输入。结果表明，所提出的混合测深方法大大提高了深度图的准确性。然后，在结合所提出的混合方法和粒子群优化（PSO）或遗传算法（GA）优化方法的基础上，引入了两种新算法，即HybF_PSO和HybF_GA。利用PSO和GA优化方法来优化计算深度模型的常数参数。与传统的比率模型相比，HybF_GA算法将-2m以下深度的测深精度从2.93提高到2.53，-2和-4m之间的深度从3.2提高到1.56，-4和-8m之间的深度从2.4提高到1.88，以及从 5.24 到 2.93 更深 - 8 m 的区域。HybF_PSO 算法比 HybF_GA 算法提高了深度超过 - 8 m 的映射区域的精度。与传统的比率模型相比，HybF_PSO 算法还大大提高了所有深度类别的测深精度。因此，可以得出结论，所提出的测深算法非常适用和有用。与传统的比率模型相比，HybF_GA算法将-2m以下深度的测深精度从2.93提高到2.53，-2和-4m之间的深度从3.2提高到1.56，-4和-8m之间的深度从2.4提高到1.88，以及从 5.24 到 2.93 更深 - 8 m 的区域。HybF_PSO 算法比 HybF_GA 算法提高了深度超过 - 8 m 的映射区域的精度。与传统的比率模型相比，HybF_PSO 算法还大大提高了所有深度类别的测深精度。因此，可以得出结论，所提出的测深算法非常适用和有用。与传统的比率模型相比，HybF_GA算法将-2m以下深度的测深精度从2.93提高到2.53，-2和-4m之间的深度从3.2提高到1.56，-4和-8m之间的深度从2.4提高到1.88，以及从 5.24 到 2.93 更深 - 8 m 的区域。HybF_PSO 算法比 HybF_GA 算法提高了深度超过 - 8 m 的映射区域的精度。与传统的比率模型相比，HybF_PSO 算法还大大提高了所有深度类别的测深精度。因此，可以得出结论，所提出的测深算法非常适用和有用。和更深的区域 - 8 m 从 5.24 到 2.93。HybF_PSO 算法比 HybF_GA 算法提高了深度超过 - 8 m 的映射区域的精度。与传统的比率模型相比，HybF_PSO 算法还大大提高了所有深度类别的测深精度。因此，可以得出结论，所提出的测深算法非常适用和有用。和更深的区域 - 8 m 从 5.24 到 2.93。HybF_PSO 算法比 HybF_GA 算法提高了深度超过 - 8 m 的映射区域的精度。与传统的比率模型相比，HybF_PSO 算法还大大提高了所有深度类别的测深精度。因此，可以得出结论，所提出的测深算法非常适用和有用。