This research paper focuses on a water quality prediction model which requires high-quality data. In the process of construction and operation of smart water quality monitoring systems based on Internet of Things (IoT), more and more big data are produced at a high speed, which has made water quality data complicated. Taking advantage of the good performance of long short-term memory (LSTM) deep neural networks in time-series prediction, a drinking-water quality model was designed and established to predict water quality big data with the help of the advanced deep learning (DL) theory in this paper. The drinking-water quality data measured by the automatic water quality monitoring station of Guazhou Water Source of the Yangtze River in Yangzhou were utilized to analyze the water quality parameters in detail, and the prediction model was trained and tested with monitoring data from January 2016 to June 2018. The results of the study indicate that the predicted values of the model and the actual values were in good agreement and accurately revealed the future developing trend of water quality, showing the feasibility and effectiveness of using LSTM deep neural networks to predict the quality of drinking water.

Semantic segmentation enables pixel-level scene understanding, and deploying it on resource-constrained platforms such as unmanned aerial vehicles and embedded sensing devices has become increasingly important. In complex scenes, however, objects with substantial scale variation and heterogeneous morphology often lead to missed small objects, discontinuities along slender structures, and incomplete boundaries. To address the challenges, this paper proposes a lightweight semantic segmentation network, named MAF-Net. The network adopts MobileNet as the backbone and enhances the representation of objects with different scales and morphological characteristics through multi-scale feature extraction and top-down feature fusion, while maintaining low model complexity. To further accommodate variations in object scale and structure, a Morphology-Adaptive Kernel Decoder (MAKD) is designed. The module introduces large-kernel, small-kernel, horizontal-strip, and vertical-strip convolution branches, and adaptively fuses the multi-branch features through a gating mechanism, thereby providing complementary contextual representations with different receptive-field scales and directional sensitivities. In addition, a Triple Feature Fusion Refinement Module (TFFRM) is constructed to further refine the initial segmentation results by integrating shallow spatial details, learned boundary-aware responses, and high-level semantic features, thereby alleviating problems such as discontinuities in slender structures and blurred object boundaries. Experimental results show that the proposed method achieves favorable overall segmentation performance on the COCO2017-derived ball-sports dataset, UDD6, and WHDLD, while maintaining a relatively small parameter count and high measured inference speed.

Multi-agent path planning (MAPP) under partial observability requires agents to coordinate their movements and complete tasks efficiently without access to global information. The planning space and coordination complexity grow rapidly with increasing numbers of agents, targets, and obstacles. We formulate large-scale MAPP as a partially observable networked Markov decision process. Based on this formulation, we propose a decentralized model-based Actor-Critic using the Kronecker-factored trust region (DM-ACKTR) algorithm. The algorithm integrates local model learning with ACKTR-based policy optimization in an independent learning architecture. Each agent learns a local model to predict the next observation and reward. These predictions are used to construct additional transitions for Actor and Critic updates. A neighborhood-based communication mechanism incorporates information from nearby agents into value estimation. Region partitioning reduces each agent’s effective planning space. These improvements enable DM-ACKTR to continue outperforming the baseline algorithms as the scale of the MAPP problem increases. Experiments across three training and five evaluation scenarios show that DM-ACKTR achieves the best overall performance. Among the five evaluated algorithms, it consistently obtains the highest TCR and lowest CR, improving TCR by 2.06–4.35% and reducing CR by 11.26–25.95% relative to the respective best baselines.