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Irrigation water is the main type of water consumption in the Yanqi Basin irrigation district of Xinjiang, which is an oasis-type irrigation district in the arid region of Northwest China. With the continuous expansion of cultivated areas, there is an increasing demand for irrigation water, resulting in an irrigation efficiency paradox and the phenomenon of "the more water-saving, the more water-scarce". In this study, the water balance method and the improved IWMI (International Water Management Institute) water balance method were used with remote sensing and statistical data from 1980 to 2020 to analyze the changes in the irrigation water supply, consumption, and loss for improvement in irrigation water use efficiency (IWUE) in the Yanqi Basin. The results showed that there was an upward trend in the cultivated land area in the irrigation district of Yanqi Basin, as monitored with remote sensing from 1980 to 2020, and the values from the remote sensing data were higher than those from the yearbooks. According to the remote sensing data, the arable land area in the irrigation district increased from 1672 km2 in 1980 to 2494 km2 in 2020, thus showing a trend of expansion. The traditional water use efficiency in the irrigation district showed an increasing trend. The lowest value for the field water-use coefficient was 0.70 in 1998, and it exceeded to 0.81 from 2009 to 2020. The canal water-use coefficient was as low as 0.50 in 1998 and increased from 0.54 in 2009 to 0.82 in 2020. The irrigation water-use coefficient increased from 0.35 in 1998 to 0.68 in 2020, with a general upward trend. In this study, the water consumption ratio indicator DFg (depleted fraction), determined using the improved water balance method, increased from 0.8390 in 1980 to 0.8562 in 2020, although it showed an overall decreasing trend, and the average was 0.8436. Cultivated land's actual irrigation water consumption per unit area reached the highest value of 8.41 × 106 m3/hm2/a in 2011 and the minimum value of 4.01 × 106 m3/hm2/a in 2020, and from 1980 to 2020 it showed a decreasing trend, while the total water diversion showed an increasing trend due to the continuous expansion of arable land. From 1980 to 2020, water diversion into the irrigation district changed from 1.214 km3 to 1.000 km3, and it reached a maximum of 1.593 km3 in 2000; water diversion into the irrigation district showed an overall upward trend. The positive impact of the post-2000 water conservation phase with the adaptation of water-saving irrigation technology was clear, as the findings showed an increase in IWUE in the Yanqi Basin irrigation district. These results provide a theoretical basis for breaking the paradox of irrigation efficiency, which can be used in the water resource management of irrigation districts. [ABSTRACT FROM AUTHOR]

In recent years, the application of wind power forecasting in power systems has gained widespread recognition. However, most studies have only focused on improving forecasting accuracy and few have considered the stability of forecasting results. To solve these two issues simultaneously, this study proposed a novel hybrid wind power forecasting model. First, two types of Laguerre polynomials are used to construct the hybrid Laguerre neural network. Then, a multi-objective Runge–Kutta algorithm is proposed to optimize the weights of the neural network while enhancing the accuracy and stability of the forecasting. Finally, ensemble learning is introduced to further improve the forecasting capability of the model. To verify the effectiveness of the proposed hybrid forecasting model, a large number of comprehensive experiments are carried out using the wind power data of a wind farm in Xinjiang, China. The experimental results show that the proposed hybrid forecasting model had higher forecasting accuracy and better stability than other forecasting models. • A new multi-objective Runge–Kutta algorithm (MORUN) is proposed. • Test MORUN performance on MOP and ZDT test suites. • A novel hybrid wind power forecasting model is proposed. • Extensive experiments are conducted to verify the efficacy of the forecasting model. [ABSTRACT FROM AUTHOR]