Solar radiation is the main source of energy on the Earth’s surface. It is very important for the environment and ecology, water cycle and crop growth. Therefore, it is very important to obtain accurate solar radiation data. In this study, we use the highest temperature T_max, lowest temperature T_min, average temperature T_avg, wind speed U, relative humidity RH, sunshine duration H and maximum sunshine duration H_max as input variables to construct a deep learning prediction model of solar radiation in the Yellow River Basin. It is compared with the recommended and corrected values of the widely used Å-P method. The results show that: (1) The correction results of the Å-P equation are better in the upstream and downstream of the Yellow River Basin but worse in the midstream. (2) The prediction result of the deep learning model in the Yellow River Basin is far better than that of the Å-P equation using the FAO-56 recommended value. It is the best in the downstream of the Yellow River Basin: R² increases from 0.894 to 0.934; MSE, RMSE and MAE decrease by 43.12%, 27.73% and 25.80%, respectively. The upstream prediction result comes in second: R² increases from 0.888 to 0.921; MSE, RMSE and MAE decrease by 33.27%, 20.02% and 19.04%, respectively. The midstream result is the worst: R² increases from 0.869 to 0.874; MSE, RMSE and MAE decrease by −0.50%, 0.07% and 3.82%, respectively. (3) The prediction results of the deep learning model in the upstream and downstream of the Yellow River Basin are far better than those of the Å-P equation using correction. The R² in the upstream of the Yellow River Basin increases from 0.889 to 0.921. MSE, RMSE and MAE decrease by 22.11%, 11.84% and 8.94%, respectively. R² in the downstream of the Yellow River Basin increases from 0.900 to 0.934, and MSE, RMSE and MAE decrease by 13.21%, 11.40% and 5.55%, respectively. In the midstream of the Yellow River Basin, the prediction results of the deep learning model are worse than those of the Å-P equation using correction: R² increases from 0.870 to 0.874, but MSE, RMSE and MAE decrease by −24.93%, −10.83% and −11.56%, respectively.

The construction of ecological irrigation districts is of great significance to protect the Yellow River ecology and achieve sustainable development of the local ecological economy. Taking the ecological irrigation district of Helan County as the study area, a health evaluation index system of the irrigation district was established, including three primary indexes of ecological environment, modernization level, and agricultural production and benefit, and 20 secondary indexes. Then, the Topsis method, entropy weight evaluation method, fuzzy pattern recognition model, and variable fuzzy model were used to evaluate the health of the Helan ecological irrigation district. In order to avoid the one-sidedness of the evaluation results of a single evaluation method, a combined evaluation method named deviation maximization combined evaluation method was used to combine each single evaluation result. The evaluation results by the combined evaluation method showed the following: (1) The ecological health of Helan irrigation district had a trend of becoming better from 2007 to 2016. (2) The grey correlation analysis showed that the soil salt content, groundwater depth, canal lining rate, ratio of efficient water-saving irrigation area, information level of the irrigation district, water productivity, agricultural unilateral aquatic output value, irrigation water consumption per mu, and coefficient of effective utilization of farmland irrigation water were closely related to the evaluation results. (3) In order to effectively improve the ecological health of Helan irrigation districts, it is necessary to reduce soil salt content and groundwater salinity, increase canal linings, promote water-saving irrigation measures, and agricultural information construction, etc.

Drought stress induced by climate change is a major limiting factor for crop growth. Selenium (Se) is recognized as an important exogenous regulator that can mitigate drought and other abiotic stresses, but the effects of Se application at different growth stages remain unclear. In this study, greenhouse-grown tomato plants were subjected to four Se treatments (T1: control; T2: Se at seedling stage; T3: Se at flowering stage; T4: Se at both stages) combined with three irrigation regimes (W1: 50–55%, W2: 65–70%, W3: 80–85% of field capacity). The impacts of Se timing on antioxidant enzymes, osmotic regulators, and growth parameters were evaluated. Drought stress induced oxidative damage, reduced photosynthesis, and inhibited biomass accumulation, while proline content increased with drought severity. Se application showed clear growth-stage specificity: under mild stress, Se at the flowering stage most effectively enhanced antioxidant activity, regulated proline metabolism, improved photosynthetic performance, and promoted growth. Dual-stage application did not provide additional benefits. These findings indicate that applying Se during the flowering and fruiting stage is optimal for alleviating drought-induced growth inhibition in tomato. The results contribute to understanding Se-mediated drought tolerance and may support the development of stage-specific Se fertilizer management strategies.