[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126372-en":3,"doc-seo-126372-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126372,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","From Seasonality to Causality - Understanding Urban Water Usage Using Statistical and Machine Learning Models","This study examines the relationship between climate conditions and residential water usage, with emphasis on how seasonal and environmental changes shape consumption patterns. Using data from over 100,000 households across three micro-climate zones over more than five years, the research applies statistical analysis and machine learning to evaluate the role of temperature, precipitation, evapotranspiration, and location. Integrating climate and billing data for Irvine, CA, the thesis quantifies seasonal dynamics and predictive performance. Time-series models (SARIMA and LSTM) show a steady decline in overall usage since 2020. LSTM outperforms SARIMA in capturing seasonal patterns, while causal forest results indicate precipitation and evapotranspiration have strong causal effects; increased rainfall reduces usage and hotter, drier conditions increase usage. These findings support conservation strategies informed by seasonal cycles and key climate drivers.","Chapman University Digital Commons  \n\n| Electrical Engineering and Computer Science (MS) Theses | Dissertations and Theses |\n| --- | --- |\n| Spring 5-2025\u003Cbr>From Seasonality to Causality: Understanding Urban Water Usage Using Statistical and Machine Learning Models\u003Cbr>Kelsey Hawkins\u003Cbr>Chapman University, [kehawkins@chapman.edu](kehawkins@chapman.edu)\u003Cbr>Follow this and additional works at: [https://digitalcommons.chapman.edu/eecs_theses](https://digitalcommons.chapman.edu/eecs_theses)\u003Cbr> Part of the Data Science Commons, and the Hydrology Commons |  |\n\nRecommended Citation  \nK. Hawkins, \"From seasonality to causality: Understanding urban water usage using statistical and machine learning models,\" M. S. thesis, Chapman University, Orange, CA, 2025. [https://doi.org/10.36837/](https://doi.org/10.36837/)[ ](https://doi.org/10.36837/)[chapman.000647](chapman.000647)  \nThis Thesis is brought to you for free and open access by the Dissertations and Theses at Chapman University Digital Commons. It has been accepted for inclusion in Electrical Engineering and Computer Science (MS) Theses by an authorized administrator of Chapman University Digital Commons. For more information, please contact [laughtin@chapman.edu](laughtin@chapman.edu).  \nFrom Seasonality to Causality: Understanding Urban Water Usage Using Statistical and Machine Learning Models  \nA Thesis by  \nKelsey Hawkins  \nChapman University  \nOrange, CA  \nFowler School of Engineering  \nSubmitted in partial fulfillment of the requirements for the degree of  \nMasters of Science in Electrical Engineering and Computer Science  \nMay 2025  \nCommittee in charge:  \nThomas Piechota, Ph.D., Chair  \nChelsea Parlett, Ph.D.  \nYuxin Wen, Ph.D.  \nThe thesis of Kelsey Hawkins is approved.  \nThomas Piechota, Ph.D., Chair  \nChelsea Parlett, Ph.D.  \n________________________  \nYuxin Wen, Ph.D.  \nApril 2025  \nFrom Seasonality to Causality: Understanding Urban Water Usage Using Statistical and Machine Learning Models  \nCopyright © 2025  \nby Kelsey Hawkins  \nACKNOWLEDGEMENTS  \nThank you to my dog, my friends and family who put up with my chaos, my mentor Chelsea, my advisor Tom, Philz Coffee, sourdough bread (LOTS ofit), and my local Pilates studio.  \nABSTRACT  \nFrom Seasonality to Causality: Understanding Urban Water Usage Using Statistical and Machine Learning  \nModels  \nby Kelsey Hawkins  \nThis study examines the relationship between climate conditions and residential water usage, focusing on how seasonal and environmental changes influence water consumption. Utilizing data from over 100,000 households across three micro-climate zones for over a fiveyear period, we apply statistical analysis and machine learning techniques to assess the impact of temperature, precipitation, evapotranspiration, and location on water usage. By integrating climate and billing data, this research provides a data-driven approach on water usage behaviorsin Irvine, CA, in collaboration with Irvine Ranch Water District (IRWD) .  \nOur analysis utilizes time series modeling, including a Seasonal Autoregressive Integrated Moving Average (SARIMA) and Long Short-Term Memory (LSTM) model to identify seasonal trends and assess predictive power in water usage. Results indicate a steady decline in overall water usage since 2020. Geographic location also plays a role in determining water usage, ET Zone 2 on average has the highest water usage across the study period. The LSTM significantly outperforms the SARIMA model in capturing seasonal patterns of water usage. Utilizing a causal forest to assess direct causality between individual climate factors and water usage showed that precipitation and evapotranspiration have a strong causal effect on water usage. Confirming that increased rainfall leads to lower water usage, while hotter and drier weather conditions drive up water usage. Other climate factors such as humidity and wind speed show negligible direct effects once evapotranspiration is accounted for.  \nThese findings empha","cbCaiefOV13kp30V","https://ap.wps.com/l/cbCaiefOV13kp30V","pdf",2019125,9,1,55,"English","en",105,"# Acknowledgements\n# Abstract\n# Introduction\n# Research Background\n## Water Usage\n## Seasonality Influence\n## Environmental Influence\n## Statistical Models and Techniques\n## Machine Learning","[{\"question\":\"What variables does the thesis use to study residential water usage?\",\"answer\":\"The study focuses on climate conditions including temperature, precipitation, and evapotranspiration, along with geographic location across three micro-climate zones.\"},{\"question\":\"Which models are used for time-series prediction of water usage?\",\"answer\":\"The thesis uses a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and a Long Short-Term Memory (LSTM) model to capture seasonal trends and assess predictive power.\"},{\"question\":\"What do the causal findings suggest about direct drivers of water usage?\",\"answer\":\"A causal forest analysis indicates precipitation and evapotranspiration have strong causal effects: increased rainfall leads to lower water usage, while hotter and drier weather increases usage.\"}]","From Seasonality to Causality - 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