###### Abstract
The results showed that there was consistency between NTL data and statistical economic data in cold and hot spots. [PERSON] et al. [23] constructed the NTL landscape metrics using NTL data to reveal the economic development and differentiation between urban and rural areas in Fujian Province, China, from multiple perspectives. Empirical results indicated that the NTL landscape metrics were valuable indicators for analyzing the distribution and evolution of the economy. In fact, these studies only qualitatively characterized socio-economic data using NTL data but did not develop regression models to quantitatively estimate and characterize the level and differentiation of socio-economic development. It may lead to unstable and uncertain results. Instead of only using NTL data, some scholars constructed regression models by selecting the NTL indices that were highly correlated with socio-economic indicators, so as to evaluate socio-economic indicators and analyze the level and differentiation of socio-economic development [25; 26; 27; 28; 29]. For example, [PERSON] et al. [26] used the normalized total radiation index to establish linear and nonlinear regression models for the GDP of various provinces in China. The results showed that the determination coefficients of linear, power law, and logistic models were all above 0.8, laying the foundation for the estimation and prediction of GDP based on NTL data [26]. [PERSON] et al. [28] utilized the GDP growth rate to address the saturation problem of NTL data obtained from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) and established a linear regression model for county-level GDP using NTL density data. The results showed that the estimated county-level GDP was consistent with the authoritative county-level GDP statistics, providing an effective method for grasping the development of county-level economy [28]. Most of these studies tended to use a single NTL index to construct regression models for socio-economic indicators, such as the total NTL index and the average NTL index. Additionally, the established regression models were usually conventional, such as linear regression, quadratic polynomial regression, and exponential regression. This paper attempts to use NTL data to evaluate and forecast the TOVAFAF. In theory, NTL data in rural areas should be obtained and used for analysis and research. As a matter of fact, NTL data mainly come from light sources in urban and urbanized areas, while there are fewer light sources in rural areas. In this situation, conventional models established using a single NTL index may lead to the omission of important information and fail to fully reflect the potential relationship between NTL data and the TOVAFAF. [PERSON] et al. [30] showed that the correlation between NTL data and the primary industry was not obvious, and the determination coefficient of the log-linear regression model established using the optimal lighting area index was only 0.306. Despite the difficulties, it is still feasible to evaluate and forecast the TOVAFAF using NTL data. The reason is that there is a close correlation between GDP and NTL data [6; 28; 29], and the TOVAFAF is the important component of GDP. Therefore, there should be a certain correlation between the TOVAFAF and NTL data. [PERSON] et al. [16] constructed multiple NTL indices as input features and established a fitting model combining the particle swarm optimization (PSO) with the back propagation (BP) artificial neural network algorithms to evaluate the poverty level in southwestern China, which achieved ideal results. However, the BP neural network algorithm needs to continuously adjust the weight values through gradient descent algorithm during the training process, which leads to slow convergence speed and is prone to getting stuck in local minima [31]. Although the PSO algorithm is usually employed to optimize the initial weights of BP algorithm, the final weights of BP algorithm still need to be obtained through gradient descent algorithm.