Soil salinity prediction using hybrid machine learning and remote sensing in Ben Tre province on Vietnam’s Mekong River Delta

• Published in: Environmental science and pollution research international 2023-06, Vol.30 (29), p.74340-74357
• Main Authors: Nguyen, Huu Duy, Van, Chien Pham, Nguyen, Tien Giang, Dang, Dinh Kha, Pham, Thi Thuy Nga, Nguyen, Quoc-Huy, Bui, Quang-Thanh
• Format: Article
• Language: English
• Subjects: Algorithms; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Climate change; Context; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental monitoring; Environmental science; Food security; Learning algorithms; Machine learning; Monitoring; Optimization algorithms; Particle swarm optimization; Prediction models; Remote sensing; Research Article; Rivers; Root-mean-square errors; Salinity; Salinity effects; Salinization; Sea level rise; Search algorithms; Soil salinity; Soils; Waste Water Technology; Water Management; Water Pollution Control
• ISSN: 1614-7499; 0944-1344; 1614-7499
• DOI: 10.1007/s11356-023-27516-x

Soil salinization is considered one of the disasters that have significant effects on agricultural activities in many parts of the world, particularly in the context of climate change and sea level rise. This problem has become increasingly essential and severe in the Mekong River Delta of Vietnam. Therefore, soil salinity monitoring and assessment are critical to building appropriate strategies to develop agricultural activities. This study aims to develop a low-cost method based on machine learning and remote sensing to map soil salinity in Ben Tre province, which is located in Vietnam’s Mekong River Delta. This objective was achieved by using six machine learning algorithms, including Xgboost (XGR), sparrow search algorithm (SSA), bird swarm algorithm (BSA), moth search algorithm (MSA), Harris hawk optimization (HHO), grasshopper optimization algorithm (GOA), particle swarm optimization algorithm (PSO), and 43 factors extracted from remote sensing images. Various indices were used, namely, root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination ( R 2 ) to estimate the efficiency of the prediction models. The results show that six optimization algorithms successfully improved XGR model performance with an R 2 value of more than 0.98. Among the proposed models, the XGR-HHO model was better than the other models with a value of R 2 of 0.99 and a value of RMSE of 0.051, by XGR-GOA ( R 2 = 0.931, RMSE = 0.055), XGR-MSA ( R 2 = 0.928, RMSE = 0.06), XGR-BSA ( R 2 = 0.926, RMSE = 0.062), XGR-SSA ( R 2 = 0.917, 0.07), XGR-PSO ( R 2 = 0.916, RMSE = 0.08), XGR ( R 2 = 0.867, RMSE = 0.1), CatBoost ( R 2 = 0.78, RMSE = 0.12), and RF ( R 2 = 0.75, RMSE = 0.19), respectively. These proposed models have surpassed the reference models (CatBoost and random forest). The results indicated that the soils in the eastern areas of Ben Tre province are more saline than in the western areas. The results of this study highlighted the effectiveness of using hybrid machine learning and remote sensing in soil salinity monitoring. The finding of this study provides essential tools to support farmers and policymakers in selecting appropriate crop types in the context of climate change to ensure food security.