# Evaluation and Prediction of Ecological Benefits in Song-Liao River Basin
## 1 Introduction
The multi-scale combination model based on the super-efficiency relaxation measure and Malmquist productivity index evaluates the static ecological efficiency and dynamic performance of the Loess Plateau, and the simulation efficiency is higher than the traditional theoretical model [15]. The Yangtze River basin is China's main ecological conservation area and forest distribution area. The optimal weighted cross-evaluation efficiency (OWCE) model based on data standardization was used to evaluate the ecological efficiency of 11 provinces (cities) in the Yangtze River Economic Belt [16]. The study on the evaluation of ecosystem services in the Heihe River Basin shows that the expansion of Cropland, significantly the decrease of Grassland and Woodland, has a direct negative impact on the provision of ecosystem services in the study area [17]. To fully understand the spatiotemporal characteristics of regional ecological benefits, ecosystem service and quality assessments should be conducted. The ecosystem service assessment aims at quantifying and evaluating all kinds of direct and indirect services provided by natural ecosystems to human beings [18]. Meanwhile, ecosystem quality assessment is the health status and sustainability of the ecosystem to fully reflect the overall quality of the ecosystem [19]. Mainstream and accepted research methods include traditional mathematical models and machine learning models. Commonly used conventional mathematical models/mechanism process models include Invest (Integrated Value of Ecosystem Services and Trade-offs), Mimes (Multiscale Integrated Model of Ecosystem Services), and SolVES (Social Values for Ecosystem Services) [20]. With the continuous development of science and technology, the machine learning model has been the mainstream direction in recent years. Characteristics of machine learning help solve the data gap problem in an environment with insufficient data. In contrast, big data, such as remote sensing, could significantly increase the sample size and reduce uncertainty. Therefore, integrating big data, neural networks, and machine learning algorithms into the study of ecosystem services is expected to promote the development of this field into a more powerful, interdisciplinary, and extensive research field. Song-Liao River Basin (SRB) is China's most crucial natural forest distribution area and one of the three temperate forest belts in the northern hemisphere. There are many forest wetlands and swamp wetlands, among which Songhua River and Nenjiang River Basin are the concentrated distribution areas of wetlands in China. Song-Liao River Basin is also a critical commodity grain base in China, and more than 85% of the area belongs to the black soil area. Therefore, as an essential ecological security barrier in China, the ecological stability of the Song-Liao River Basin is of great significance for protecting biodiversity and ensuring food security. However, the Song-Liao River Basin is located in a semi-humid and semi-arid area, a typical climate change and ecologically fragile area. Rivers in this area are dense, but disasters such as floods and droughts appear alternately. The uneven spatial distribution of water resources leads to serious soil erosion. Therefore, it is necessary to carry out a comprehensive ecological benefit assessment of the Song-Liao River Basin. This measure will be able to provide a basis for the scientific utilization and protection of forestry, agriculture, and land resources. Based on remote sensing datasets and statistical data, this study evaluated the ecological benefits of the Song-Liao River Basin from 1990 to 2020. First, land use change is used to characterize the macro-ecological structure change. Based on water yield, soil retention, and carbon fixation, the Ecosystem Service Index (ESI) was constructed. The Remote Sensing Ecological Index (RSEI) is used to characterize the Ecosystem Quality Index (EQI).