To improve agricultural production efficiency, increase farmers’ income, and promote sustainable development, we established a multi-objective optimisation model for crop planting structure in an irrigation area using the grey wolf optimisation (GWO) algorithm to comprehensively consider the resource, economic, and social objectives associated with agriculture. This model was subsequently applied to obtain the optimal planting structure in the southern bank of the Xiaolangdi Reservoir irrigation area in Henan Province, China. The planting areas of wheat, corn, autumn miscellaneous, and economic crops are 30,417; 25,050; 7157; and 1789 hm², respectively. The irrigation water is 8292.66 × 10⁴ m³, output value of crops is 105,721.37 × 10⁴ CNY, and crop yield is 34,280.31 × 10⁴ kg. Different solutions are used to solve the model to evaluate the results, and the order degree entropy method is used to evaluate and compare the results of multiple solutions. The optimisation scheme obtained with this model is consistent with the evaluation results of the cooperative game optimisation scheme, and the relative order degree entropy is 0.136, which is better than that in other schemes. Thus, the optimisation scheme of crop planting structure obtained via GWO comprehensively considers irrigation water consumption, economic benefits, and crop yield, which ensures coordinated development of resource, economic, and social systems and is conducive to promoting the benign development of the whole irrigation area system.

Monthly runoff prediction plays a crucial role in water resource management, flood prevention, and disaster reduction. This study proposed a novel hybrid model for predicting monthly runoff by combining variational modal decomposition (VMD) with an extreme learning machine (ELM) and adaptive boosting (AdaBoost) algorithm. First, VMD is used to decompose the monthly runoff data, simplifying it and addressing its non-stationarity by extracting subsequences at different frequency scales. Next, the ELM model is applied to each subsequence within the AdaBoost algorithm to enhance prediction accuracy and stability. To contextualise its performance, the proposed model was systematically compared with four representative comparable models (VMD-ELM, ELM-AdaBoost, LSTM, and VMD-TPE-LSTM) using the same training/validation datasets (80% for training and 20% for validation) and evaluation metrics (root mean square error, RMSE; mean absolute percentage error, MAPE). The results indicate that the VMD-ELM-AdaBoost model outperforms all comparative models: at Yanshan Station, it achieves an RMSE of 2.521 mm and MAPE of 8.56% (34.8–45.1% lower RMSE than VMD-ELM, ELM-AdaBoost, and LSTM); at Baiguishan Station, it yields an RMSE of 2.906 mm and MAPE of 9.02% (22.3–42.6% lower RMSE than VMD-TPE-LSTM and other alternatives). This study demonstrates that the VMD-ELM-AdaBoost model balances accuracy, efficiency, and data adaptability, providing a practical tool for monthly runoff prediction in data-limited basins.

Centralized sewer networks are rarely feasible for scattered mountain villages across China. Their construction costs stay high, and uneven terrain easily triggers pipe blockages and infiltration. Most existing rural wastewater treatment devices run on fixed operating schedules. They maintain full aeration even during low water inflow, wasting electricity and destabilizing effluent quality. This study reports a full-scale summer field assessment of an integrated attached-growth biofilm reactor deployed at the sewage treatment station serving Miaodong and Miaoxi Villages, Ruyang County, Henan Province, China. The system combines hydrolysis acidification, two-stage biological contact oxidation, sedimentation, post-sedimentation polishing, and a cloud-connected monitoring and control module. The control system adjusts influent pumping, aeration, internal reflux, and sludge discharge in response to measured hydraulic and dissolved-oxygen signals. The design treatment capacity was 850 m³ d⁻¹. During the 25-day monitoring period, the packing filling ratio was 70%, dissolved oxygen was maintained at 2.0–4.0 mg L⁻¹, and water temperature was 20 ± 5 °C. Average COD removal reached 91.1%, ammonium nitrogen (NH₄⁺-N) removal reached 88.9%, total nitrogen (TN) removal reached 83.5%, and total phosphorus (TP) removal achieved 81.7%. The average unit electricity consumption was 0.195 kWh·m⁻³. Because no fixed-frequency reference operation was conducted under identical influent and environmental conditions, the specific energy-saving contribution of the adaptive control module could not be quantitatively isolated. The reported value should therefore be interpreted as system-level field performance rather than as a verified percentage reduction attributable exclusively to intelligent control. The average TP concentration after polishing was 0.59 mg L⁻¹, exceeding the 0.5 mg L⁻¹ Class A limit of GB 18918-2002. The results characterize summer operation under the investigated loading and temperature conditions and should not be extrapolated directly to year-round compliance, winter operation, or heavy-rainfall events.