Exploring the link between drought‐related terms and public interests: Global insights from LSTM‐based predictions and Google Trends analysis

缺水 水资源 预期寿命 人口 均方误差 地理 环境科学 统计 数学 人口学 社会学 生态学 生物
作者
Seyed Mohammad Bagher Shahabi‐Haghighi,Hossein Hamidifar
出处
期刊:Hydrological Processes [Wiley]
卷期号:37 (11) 被引量:1
标识
DOI:10.1002/hyp.15016
摘要

Abstract Effective drought monitoring is of paramount importance in hydrology. It aids in mitigating the detrimental effects of water scarcity, facilitates sustainable resource management, and informs policy decisions. Therefore, it is crucial to comprehensively comprehend the dynamics and trends of drought‐related phenomena. This study aims to explore the relationship between six low water quantity terms including drought, water crisis, water scarcity, water shortage, water stress, and water insecurity and some socio‐economic, geographic, and demographic parameters for different regions of the world and to predict the future trend of public interest in the mentioned terms using Long Short‐Term Memory neural network (LSTM) models. Google Trend data analysis was used to examine the public interest in these terms from 2017 to 2022. The LSTM models were trained using historical data on the studied terms, and their performance was evaluated using Root Mean Square Error (RMSE) indicator. The Google Trend data analysis showed that public interest in water shortage and water insecurity increased significantly from 2017 to 2022. The LSTM models showed promising results for predicting future trends in the mentioned water related issues, with RMSE scores (dimensionless) ranging from 0.04 to 0.43. The most significant socio‐economic, geographic and demographic parameters were found to be population, life expectancy, access to drinking water, and access to Internet while there was no correlation between precipitation and searched terms. The results suggest that LSTM models can be an effective tool for forecasting water related issues and emphasizes the importance of socio‐economic, geographic and demographic parameters in the public search behaviour around the world. The study also highlights the increasing public awareness of water related issues and the need for sustainable water management practices, particularly in regions with high water shortage and insecurity. The LSTM‐based prediction models in this study have practical applications in early warning systems for droughts, community education on water conservation, prioritizing vulnerable areas, assessing public perception of climate change's relation to droughts, and evaluating water policies. Further research is needed to improve the accuracy of these models incorporating the effective parameters and to develop effective strategies for managing water resources in regions facing water scarcity.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

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
汉弗里戴维完成签到,获得积分10
刚刚
笨笨的怜南完成签到,获得积分10
1秒前
cavendipeng完成签到,获得积分10
1秒前
今后应助cmc12314采纳,获得10
1秒前
芋圆完成签到,获得积分10
1秒前
无私的颤完成签到,获得积分10
1秒前
fabian完成签到,获得积分10
2秒前
2秒前
妮妮完成签到,获得积分10
3秒前
浓浓的淡淡完成签到 ,获得积分10
3秒前
YMY完成签到,获得积分20
4秒前
奋斗人雄完成签到,获得积分10
4秒前
wdwd完成签到,获得积分10
4秒前
依然At发布了新的文献求助10
5秒前
炙热初晴完成签到,获得积分10
5秒前
丘比特应助baobeikk采纳,获得10
5秒前
5秒前
重要的道之完成签到 ,获得积分20
5秒前
6秒前
鸭嘴兽发布了新的文献求助10
6秒前
魔幻高烽完成签到,获得积分10
6秒前
7秒前
缥缈逍遥完成签到 ,获得积分10
8秒前
8秒前
神仙渔完成签到,获得积分10
8秒前
小诸葛完成签到 ,获得积分10
9秒前
无语的从云完成签到,获得积分10
10秒前
虚幻的海安完成签到,获得积分10
10秒前
ZYC007完成签到,获得积分10
12秒前
依然At完成签到,获得积分10
12秒前
丘比特应助神仙渔采纳,获得10
12秒前
研友_LX7478完成签到,获得积分10
12秒前
13秒前
13秒前
13秒前
FashionBoy应助小蚊子采纳,获得20
13秒前
吉尼太美完成签到,获得积分10
13秒前
13秒前
孙新月发布了新的文献求助10
14秒前
从容的戎完成签到,获得积分10
15秒前
高分求助中
Sustainability in Tides Chemistry 2800
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
Rechtsphilosophie 1000
Bayesian Models of Cognition:Reverse Engineering the Mind 888
Le dégorgement réflexe des Acridiens 800
Defense against predation 800
A Dissection Guide & Atlas to the Rabbit 600
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3134083
求助须知:如何正确求助?哪些是违规求助? 2784918
关于积分的说明 7769341
捐赠科研通 2440444
什么是DOI,文献DOI怎么找? 1297415
科研通“疑难数据库(出版商)”最低求助积分说明 624959
版权声明 600792