Water resource modeling is an important means of studying the distribution, change, utilization, and management of water resources. By establishing various models, water resources can be quantitatively described and predicted, providing a scientific basis for water resource management, protection, and planning. Traditional hydrological observation methods, often reliant on experience and statistical methods, are time-consuming and labor-intensive, frequently resulting in predictions of limited accuracy. However, machine learning technologies enhance the efficiency and sustainability of water resource modeling by analyzing extensive hydrogeological data, thereby improving predictions and optimizing water resource utilization and allocation. This review investigates the application of machine learning for predicting various aspects, including precipitation, flood, runoff, soil moisture, evapotranspiration, groundwater level, and water quality. It provides a detailed summary of various algorithms, examines their technical strengths and weaknesses, and discusses their potential applications in water resource modeling. Finally, this paper anticipates future development trends in the application of machine learning to water resource modeling.

Accurate and efficient flower identification holds significant importance not only for the general public—who may use this information for educational, recreational, or conservation purposes—but also for professionals in fields such as botany, agriculture, and environmental science, where precise flower recognition can assist in biodiversity assessments, crop management, and ecological monitoring. In this study, we propose a novel flower recognition method utilizing a masked autoencoder, which leverages the power of self-supervised learning to enhance the model’s feature extraction capabilities, resulting in improved classification performance with an accuracy of 99.6% on the Oxford 102 Flowers dataset. Consequently, we have developed a large-scale masked autoencoder pre-training model specifically tailored for flower identification. This approach allows the model to learn robust and discriminative features from a vast amount of unlabeled flower images, thereby enhancing its generalization ability for flower classification tasks. Our method has been applied successfully to flower target detection, achieving a Mean Average Precision (mAP) of 71.3%. This result underscores the versatility and effectiveness of our approach across various flower-related tasks, including both detection and recognition. Simultaneously, we have developed a straightforward, user-friendly flower recognition and classification software application, which offers convenient and reliable references for flower education, teaching, dataset annotation, and other uses.

This study addresses the challenges in plant pest and disease prediction within the context of smart agriculture, highlighting the need for efficient data processing techniques. In response to the limitations of existing models, which are characterized by slow training speeds and a low prediction accuracy, we introduce an innovative prediction method that integrates gene expression programming (GEP) with support vector machines (SVM). Our approach, the gene expression programming—support vector machine (GEP-SVM) model, begins with encoding and fitness function determination, progressing through cycles of selection, crossover, mutation, and the application of a convergence criterion. This method uniquely employs individual gene values as parameters for SVM, optimizing them through a grid search technique to refine genetic parameters. We tested this model using historical data on wheat blossom midges in Shaanxi Province, spanning from 1933 to 2010, and compared its performance against traditional methods, such as GEP, SVM, naive Bayes, K-nearest neighbor, and BP neural networks. Our findings reveal that the GEP-SVM model achieves a leading back-generation accuracy rate of 90.83%, demonstrating superior generalization and fitting capabilities. These results not only enhance the computational efficiency of pest and disease prediction in agriculture but also provide a scientific foundation for future predictive endeavors, contributing significantly to the optimization of agricultural production strategies.