We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
A WebGIS-Based System for Supporting Saline–Alkali Soil Ecological Monitoring: A Case Study in Yellow River Delta, China
Summary
Researchers developed a web-based geographic information system for monitoring and predicting soil ecological conditions in the Yellow River Delta region of China, an area affected by saline-alkali soils. The system uses machine learning models to assess soil health indicators and provides online visualization and prediction tools. This platform could help land managers make more informed decisions about agricultural practices and environmental risk reduction in vulnerable soil ecosystems.
Monitoring and evaluation of soil ecological environments are very important to ensure saline–alkali soil health and the safety of agricultural products. It is of foremost importance to, within a regional ecological risk-reduction strategy, develop a useful online system for soil ecological assessment and prediction to prevent people from suffering the threat of sudden disasters. However, the traditional manual or empirical parameter adjustment causes the mismatch of the hyperparameters of the model, which cannot meet the urgent need for high-performance prediction of soil properties using multi-dimensional data in the WebGIS system. To this end, this study aims to develop a saline–alkali soil ecological monitoring system for real-time monitoring of soil ecology in the Yellow River Delta, China. The system applied advanced web-based GIS, including front-end and back-end technology stack, cross-platform deployment of machine learning models, and a database embedded in multi-source environmental variables. The system adopts a five-layer architecture and integrates functions such as data statistical analysis, soil health assessment, soil salt prediction, and data management. The system visually displays the statistical results of air quality, vegetation index, and soil properties in the study area. It provides users with ecological risk assessment functions to analyze heavy metal pollution in the soil. Specially, the system introduces a tree-structured Parzan estimator (TPE)-optimized machine learning model to achieve accurate prediction of soil salinity. The TPE–RF model had the highest prediction accuracy (R2 = 94.48%) in the testing set in comparison with the TPE–GBDT model, which exhibited a strong nonlinear relationship between environmental variables and soil salinity. The system developed in this study can provide accurate saline–alkali soil information and health assessment results for government agencies and farmers, which is of great significance for agricultural production and saline–alkali soil ecological protection.
Sign in to start a discussion.
More Papers Like This
Soil Salinity Weakening and Soil Quality Enhancement after Long-Term Reclamation of Different Croplands in the Yellow River Delta
Researchers examined how long-term agricultural reclamation of saline soils in China's Yellow River Delta affected soil salinity and quality, finding that reclamation progressively reduced salinity and improved soil quality indices across different cropping systems.
Soil Salt and Water Regulation in Saline Agriculture Based on Physical Measures with Model Analysis
This study developed a model-based approach to optimize water and salt regulation in saline agricultural soils in the Yellow River Delta, finding that targeted irrigation management strategies can improve root zone conditions for crops in areas with shallow saline groundwater.
Water Quality Monitoring And Ground Water Level Prediction Using Machine Learning
Researchers applied machine learning techniques to water quality monitoring and groundwater level prediction, demonstrating the potential of data-driven approaches for environmental sensing and resource management.
Web-Based Information and Analytical Monitoring System Tools – Online Visualization and Analysis of Surface Water Quality of Mining and Chemical Enterprises
Researchers developed a web-based information and analytical monitoring system for visualizing and analyzing surface water quality data from a Ukrainian mining and chemical enterprise, providing tools to track contamination trends and forecast environmental changes in real time.
Approaches for the sustainable management of the Apulian coastal areas: the potential of a geoportal
Researchers developed a WebGIS platform to help manage coastal sediment erosion along the Apulian coast of Italy. The tool integrates geographic data to identify areas of high sediment production and support land-use planning decisions that reduce coastal erosion risk. This approach offers a low-impact way to improve the resilience of coastal systems in the face of climate change.