干旱区生态环境知识资源中心(Arid): New strategy based on Hammerstein-Wiener and supervised machine learning for identification of treated wastewater salinization in Al-Hassa region, Saudi Arabia

Arid

DOI	10.1186/s12302-024-00914-9
	New strategy based on Hammerstein-Wiener and supervised machine learning for identification of treated wastewater salinization in Al-Hassa region, Saudi Arabia
	Shah, Syed Muzzamil Hussain; Abba, Sani I.; Yassin, Mohamed A.; Lawal, Dahiru U.; Aliyu, Farouq; Al-Qadami, Ebrahim Hamid Hussein; Qureshi, Haris U.; Aljundi, Isam H.; Asmaly, Hamza A.; Sammen, Saad Sh.; Scholz, Miklas
通讯作者	Abba, SI
来源期刊	ENVIRONMENTAL SCIENCES EUROPE
ISSN	2190-4707
EISSN	2190-4715
出版年	2024
卷号	36 期号:1
英文摘要	The agricultural sector faces challenges in managing water resources efficiently, particularly in arid regions dealing with water scarcity. To overcome water stress, treated wastewater (TWW) is increasingly utilized for irrigation purpose to conserve available freshwater resources. There are several critical aspects affecting the suitability of TWW for irrigation including salinity which can have detrimental effects on crop yield and soil health. Therefore, this study aimed to develop a novel approach for TWW salinity prediction using artificial intelligent (AI) ensembled machine learning approach. In this regard, several water quality parameters of the TWW samples were collected through field investigation from the irrigation zones in Al-Hassa, Saudi Arabia, which were later assessed in the lab. The assessment involved measuring Temperature (T), pH, Oxidation Reduction Potential (ORP), Electrical Conductivity (EC), Total Dissolved Solids (TDS), and Salinity, through an Internet of Things (IoT) based system integrated with a real-time monitoring and a multiprobe device. Based on the descriptive statistics of the data and correlation obtained through the Pearson matrix, the models were formed for predicting salinity by using the Hammerstein-Wiener Model (HWM) and Support Vector Regression (SVR). The models' performance was evaluated using several statistical indices including correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), and root mean square error (RMSE). The results revealed that the HWM-M3 model with its superior predictive capabilities achieved the best performance, with R2 values of 82% and 77% in both training and testing stages. This study demonstrates the effectiveness of AI-ensembled machine learning approach for accurate TWW salinity prediction, promoting the safe and efficient utilization of TWW for irrigation in water-stressed regions. The findings contribute to a growing body of research exploring AI applications for sustainable water management.
英文关键词	Salinization Hammerstein-Wiener model Support vector regression Artificial intelligence Machine learning SDG6
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001246006100001
WOS关键词	IRRIGATION ; SVM
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403671
推荐引用方式 GB/T 7714	Shah, Syed Muzzamil Hussain,Abba, Sani I.,Yassin, Mohamed A.,et al. New strategy based on Hammerstein-Wiener and supervised machine learning for identification of treated wastewater salinization in Al-Hassa region, Saudi Arabia[J],2024,36(1).
APA	Shah, Syed Muzzamil Hussain.,Abba, Sani I..,Yassin, Mohamed A..,Lawal, Dahiru U..,Aliyu, Farouq.,...&Scholz, Miklas.(2024).New strategy based on Hammerstein-Wiener and supervised machine learning for identification of treated wastewater salinization in Al-Hassa region, Saudi Arabia.ENVIRONMENTAL SCIENCES EUROPE,36(1).
MLA	Shah, Syed Muzzamil Hussain,et al."New strategy based on Hammerstein-Wiener and supervised machine learning for identification of treated wastewater salinization in Al-Hassa region, Saudi Arabia".ENVIRONMENTAL SCIENCES EUROPE 36.1(2024).