The imbalance between water supply and demand (WSD) has been growing noticeable as a result of the economy’s fast expansion which can be effectively alleviated using optimal allocation of water resources. An urban water resources allocation (WRA) model based on the innovative Multi-Objective Nutcracker Optimization Algorithm (MONOA) is proposed in this study. Taking into account economic, social and ecological benefits, a comprehensive multi-objective optimization (MOO) model is established. By introducing the opposite learning strategy, non-dominated sorting approach and crowding distance mechanism to a recently reported intelligent optimization algorithm called the Nutcracker Optimization Algorithm (NOA), the novel nature-inspired metaheuristic algorithm MONOA is proposed to solve the multi-objective optimization model. The MONOA is evaluated on ten benchmark test functions, and it exhibits superior distribution and convergence by comparing with some highly cited algorithms. The proposed model is applied to Handan, China, in order to obtain a reasonable water allocation scheme in the planning year. The simulation results reveal that the economic benefit is in the range CNY [1.36, 1.44] × 10¹¹, water shortage is in the range [0.66, 0.98] × 10⁸ m³ and COD emission is in the range [3.70, 3.91] × 10⁴ t in all the obtained Pareto solutions. The water resources management departments might create customized water allocation plans by balancing different goals and taking preferences into account. Moreover, the proposed method is a general approach that can be applied to many other cities. Hence, it is of great significance to the sustainable development and utilization of urban water resources.

Atmospheric ducts play a critical role in the propagation of electromagnetic waves by minimizing signal loss and extending transmission distances, which is essential for radar, communication, and navigation systems. This study leverages meteorological sounding data and reanalysis data to analyze the distribution of atmospheric ducts in the Bohai Sea and Yellow Sea regions of China. The parabolic equation method was employed to simulate the propagation characteristics of electromagnetic waves in evaporation ducts, surface ducts, and mixed duct environments, focusing on the effects of electromagnetic wave frequency and antenna height. In the Bohai Sea region, the height of evaporation ducts peaks at 13 m in spring and autumn, decreasing to 6 m in winter. In the Yellow Sea region, the height reaches 12 m in autumn and drops to 7 m in summer, indicating a heterogeneous distribution. The monthly mean occurrence rate of atmospheric ducts is defined as the number of atmospheric duct events in a given month divided by the total number of samples for that month. Influenced by the summer and winter monsoons, the occurrence rate of surface ducts is higher from May to September and lower from October to April of the following year. In contrast, elevated ducts reach their peak occurrence rate of 60% in October. In an evaporation duct environment, propagation loss gradually increases with distance, and the loss is more pronounced in non-uniform environments. In surface ducts, propagation loss exhibits periodic fluctuations with distance, exceeding 47 dB. The mixed duct environment integrates the characteristics of both evaporation and surface ducts, effectively filling the shadow zone between 10 m and 70 m.

Evaporation Duct Height (EDH) is a crucial parameter in evaporation duct modeling, as it directly influences the strength of the waveguide trapping effect and significantly impacts the over-the-horizon detection performance of maritime radars. To address the limitations of low prediction accuracy and limited interpretability in existing deep learning models under complex marine meteorological conditions, this study proposes a surrogate model, BLA-EDH, designed to emulate the output of the Naval Postgraduate School (NPS) model for real-time EDH estimation. Experimental results demonstrate that BLA-EDH can effectively replace the traditional NPS model for real-time EDH prediction, achieving higher accuracy than Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) models. Random Forest analysis identifies relative humidity (0.2966), wind speed (0.2786), and 2-m air temperature (0.2409) as the most influential environmental variables, with importance scores exceeding those of other factors. Validation using the parabolic equation shows that BLA-EDH attains excellent fitting performance, with coefficients of determination reaching 0.9999 and 0.9997 in the vertical and horizontal dimensions, respectively. This research provides a robust foundation for modeling radio wave propagation in the Yellow Sea and Bohai Sea regions and offers valuable insights for the development of marine communication and radar detection systems.

Radio noise at mountainous observatories affects the deployment of radio observation equipment. Existing mountainous-station measurements have mainly focused on spectrum occupancy and interference amplitude, while the statistical properties of the noise remain insufficiently addressed. To fill this gap, this study analyzes both the Gaussian and impulsive components of the noise. Radio noise in the 30–500 MHz band was measured at a high-altitude mountainous station in southwestern China using a USRP X310 software-defined radio. For the Gaussian component, the measured noise figure exceeds the ITU-R P.372-17 model and decreases with frequency. A smoothing spline model achieved an RMSE of 0.8687, an MAE of 0.3843, and an R² of 0.9929, outperforming the ITU-R P.372-17 model. For the impulsive component, pulse events were concentrated in the 30–140 MHz and 200–270 MHz bands. An XGBoost regression model was trained to predict IN level density and pulse length from statistical features and was benchmarked against linear, polynomial, and random forest baselines under leave-one-time-block-out cross-validation. These results provide empirical data and references for radio service deployment at mountainous observation stations.