{"id":2,"date":"2023-06-19T20:10:49","date_gmt":"2023-06-19T20:10:49","guid":{"rendered":"https:\/\/faculty.engineering.asu.edu\/txu\/?page_id=2"},"modified":"2024-12-04T00:33:57","modified_gmt":"2024-12-04T00:33:57","slug":"sample-page","status":"publish","type":"page","link":"https:\/\/labs.engineering.asu.edu\/txu\/","title":{"rendered":"Home"},"content":{"rendered":"
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Groundwater Sustainability and Data Sciences<\/span><\/h1>\n\n<\/div><\/div>\n\n\n
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Our research group combines process-based models with data-driven methods to improve predictive capability and understanding of water resources systems, groundwater in particular, under human adaptations and global change. Recent projects include hybrid process-based and deep learning modeling of a snow dominated mountainous karst watershed, using remote sensing and machine learning for crop irrigation monitoring, and regional scale viability assessment of agricultural managed aquifer recharge.<\/p>\n<\/div>\n\n\n\n

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Contact<\/h3>\n\n\n\n

Tianfang Xu
School of Sustainable Engineering & The Built Environment<\/a>
Arizona State University | Ira A. Fulton Schools of Engineering
Office: ISTB7, Room 412
Email:\u00a0tianfang.xu@asu.edu<\/a>
Phone: (480) 965-7666<\/p>\n<\/div>\n\n\n\n

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Our research group combines process-based models with data-driven methods to improve predictive capability and understanding of water resources systems, groundwater in particular, under human adaptations and global change. Recent projects include hybrid process-based and deep learning modeling of a snow dominated mountainous karst watershed, using remote sensing and machine learning for crop irrigation monitoring, and…<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-2","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"\nHome - Groundwater Sustainability and Data Sciences<\/title>\n<meta name=\"description\" content=\"Our research group combines process-based models with data-driven methods to improve predictive capability and understanding of water resources systems, groundwater in particular, under human adaptations and global change.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/labs.engineering.asu.edu\/txu\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Home - 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