亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

A WebGIS-Based System for Supporting Saline–Alkali Soil Ecological Monitoring: A Case Study in Yellow River Delta, China

环境科学 土壤盐分 计算机科学 环境资源管理 水文学(农业) 土壤水分 土壤科学 地质学 岩土工程
作者
Yingqiang Song,Yinxue Pan,Meiyan Xiang,Weihao Yang,Dexi Zhan,Xingrui Wang,Miao Lu
出处
期刊:Remote Sensing [Multidisciplinary Digital Publishing Institute]
卷期号:16 (11): 1948-1948 被引量:2
标识
DOI:10.3390/rs16111948
摘要

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.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
2秒前
寒冷高山发布了新的文献求助10
7秒前
冷傲的雪兰完成签到,获得积分10
15秒前
科研通AI6.2应助meimei采纳,获得10
19秒前
20秒前
22秒前
22秒前
22秒前
香蕉觅云应助科研通管家采纳,获得10
22秒前
领导范儿应助科研通管家采纳,获得10
22秒前
yunsww发布了新的文献求助10
25秒前
宋佳完成签到,获得积分10
36秒前
科研通AI6.2应助meimei采纳,获得10
37秒前
伯云完成签到,获得积分10
43秒前
邱蔓莉完成签到,获得积分10
47秒前
邱蔓莉发布了新的文献求助20
51秒前
健康的寒天完成签到,获得积分10
53秒前
Akim应助寒冷高山采纳,获得10
55秒前
英姑应助lulu采纳,获得10
1分钟前
乐乐应助lian采纳,获得10
1分钟前
1分钟前
听音乐的可可完成签到 ,获得积分10
1分钟前
科研通AI6.3应助dagger采纳,获得10
1分钟前
1分钟前
lian发布了新的文献求助10
1分钟前
络噬元兽完成签到 ,获得积分10
1分钟前
1分钟前
1分钟前
1分钟前
寒冷高山发布了新的文献求助10
1分钟前
Ji完成签到,获得积分10
1分钟前
有才的老妖怪完成签到 ,获得积分10
1分钟前
1分钟前
传奇3应助冷酷依萱采纳,获得10
1分钟前
英姑应助冷酷依萱采纳,获得10
1分钟前
aaqaq123321发布了新的文献求助10
1分钟前
1分钟前
dagger发布了新的文献求助10
1分钟前
1分钟前
1分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Chemistry and Physics of Carbon Volume 18 800
The Organometallic Chemistry of the Transition Metals 800
The formation of Australian attitudes towards China, 1918-1941 640
Signals, Systems, and Signal Processing 610
Development Across Adulthood 600
天津市智库成果选编 600
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6444288
求助须知:如何正确求助?哪些是违规求助? 8258194
关于积分的说明 17590917
捐赠科研通 5503260
什么是DOI,文献DOI怎么找? 2901308
邀请新用户注册赠送积分活动 1878358
关于科研通互助平台的介绍 1717603