Predicting Soil Salinity in the Red River Delta (Vietnam) Using Machine Learning and Assessing Farmers' Adaptive Capacity

Abstract. Soil salinity is a grave environmental threat to agricultural development and food security in large parts of the world, especially in the situation of global warming and sea level rise. Reliable information on the adaptive capacity of farms plays a key role in reducing the socioeconomic effects of soil salinization and helps policymakers and farmers propose more appropriate measures to combat the phenomenon. The research aims to design a theoretical framework to assess soil salinity in the Red River Delta (Vietnam) based on machine learning, optimization algorithms (namely, Xgboost (XGB), XGB-Pelican Optimization Algorithm (POA), XGB-Siberian Tiger Optimization (STO), XGB-Serval Optimization Algorithm (SOA), XGB-Particle Swarm Optimization (PSO), and XGB-Grasshopper Optimization Algorithm (GOA)), remote sensing, and interviews with local people. We evaluated the geographical distribution of soil salinity by applying machine learning to Sentinel 1 and 2A. The adaptive capacity of farmers was evaluated through interviews with 87 households. The statistical indices, namely the mean absolute error (MAE), the root mean square error (RMSE), and the correlation coefficient (R2), were used to assess the machine learning models. The outcome of this study demonstrated that all optimization algorithms were successful in improving the accuracy of the XGB model. The XGB-POA had the most performance, with an R2 value of 0.968, followed by XGB-STO (R2=0.967), XGB-SOA (R2=0.966), XGB-PSO (R2=0.964), and XGB-GOA (R2=0.964), respectively. The soil salinity map produced by the proposed models also indicated that the coastal and riverside regions were the most affected by soil salinity. The results also showed human and financial resources to be the two most important factors influencing the adaptive capacity of farmers. This study provides a key theoretical framework that enhances previous previous and can assist policymarkers and farmers in managing land resource, such as accurately identifying areas affected by soil salinity for agricultural development in the context of climate change. In addition, this research highlights the importance of integrating machine learning, remote sensing, and socio-economic surveys in soil salinity management, which can support farmers for sustainable agricultural development.