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Three-dimensional (3D) mineral prospectivity mapping (MPM) uses mathematical models to integrate different types of 3D data related to mineralization to obtain mineral prospectivity information in 3D space. Existing geological data contain known deposits, non-deposits and unknown ore-bearing data, corresponding to positive samples, negative samples and unlabeled samples respectively in MPM. Different sample combination types require different mathematical models. In this paper, support vector machine class (SVMC) machine learning method is selected to compare the influence of different sample combination types on prediction results. The SVMC is a one-class SVM (OCSVM) model based on positive-only samples, the SVM is based on both positive and negative samples, and the bagging-based positive-unlabeled learning algorithm with SVM base learner (BPUL-SVM) is based on both positive and unlabeled samples. The study area is in the Sanshandao-Cangshang offshore and onshore Au district, where there are Sanshandao, Cangshang and Xinli large- and super-large-scale Au deposits. Moreover, the discovery of large-scale Sea Au deposits in the sea area indicates the great potential for mineralization in the district. According to the metallogenic geological characteristics, the Au deposits in the Sanshandao-Cangshang district are controlled by the NE-striking fault and are closely related to the Linglong intrusions and Guojialing intrusions. The ore-bearing intrusion shows low density and low-moderate magnetic susceptibility. Because the Au orebodies hosted in the Sanshandao fault and its secondary faults, the NE-striking faults are key to delineating the targets. In this paper, weights of evidence (WofE), OCSVM, SVM and BPUL-SVM are used to MPM, and the prediction-area (P-A) plot method is used to delineate the targets. According to the ROC curve, F1 score and P-A plot evaluation methods, the model performance from high to low is BPUL-SVM13, SVM12, WofE and OCSVM. The BPUL-SVM model performance with samples combination types of positive samples and unlabeled samples was optimum in SVMC prediction models. The Markov chain Monte Carlo (MCMC) simulation and return-risk evaluation model are used to evaluate the return and risk of the targets and finally determine the I-level targets with high return and low risk. The delineated targets are mainly distributed along the F2 and F3 faults (Sanshandao-Cangshang fault). Combined with the mineralization regularity, the deep and periphery space of the known deposits are important to explore Au orebodies. The delineated targets are important to explore offshore and onshore Au orebodies in the Sanshandao-Cangshang district. [ABSTRACT FROM AUTHOR]

This study represents the first application of Sentinel-2 remote sensing imagery and model fusion techniques to assess the chlorophyll-a (Chla) concentration and turbidity in Nansi Lake, Shandong Province, China, from 2016 to 2022. First, we innovatively employed the stacking method to fuse eight fundamentally different Machine Learning (ML) models, each utilising 20 and 17 feature bands, resulting in the development of a robust algorithm for estimating the Chla concentration and turbidity in Nansi Lake. The results demonstrate that the Stacking Model has achieved outstanding theoretical generalisation capability. Second, the sensitivity of the model to extreme value data in the sample was quantified, and we found that compared with extreme gradient boosting (XGBoost), the optimal performance of the Stacking Model improved by 12%, to some extent, it solved the problem of high-value underestimation and low-value overestimation. The SHapley Additive exPlanations (SHAP) results revealed that features such as Three Bands, Enhanced Three, R rs 492 / R rs 560 , R rs 705 / R rs 665 play a crucial role in estimating Chla concentration. For the turbidity estimation, the Normalized Difference Turbidity Index (NDTI), R rs 705 + R rs 560 , R rs 865 - R rs 740 made significant contributions. Finally, we utilised the Stacking Model to create spatiotemporal maps of the Chla concentration and turbidity in Nansi Lake from 2016 to 2022. We analysed the causes of the water quality changes and explored the driving factors. Compared with previous studies, this paper provides a new idea for the monitoring of lake water quality parameters by using the high resolution of Sentinel-2 image and the high precision of model fusion technology, these results can provide a reference for similar water area research and decision-making support for environment-related departments. • Innovatively fusion 8 algorithms with different principles, a robust algorithm suitable for estimating Chla and turbidity has been developed. • The SHAP was used to identify the key variables for Chla and turbidity of the lake. • Remote sensing mapping of Chla and turbidity in Nansi Lake, for the years 2016-2022 on quarterly and annual bases. • We analysed the spatiotemporal variations in the water quality of Nansi Lake, from 2016 to 2022. [ABSTRACT FROM AUTHOR]