{"id":12866,"date":"2025-05-06T15:56:46","date_gmt":"2025-05-06T22:56:46","guid":{"rendered":"https:\/\/elmontgomery.com\/?p=12866"},"modified":"2025-12-30T14:42:06","modified_gmt":"2025-12-30T21:42:06","slug":"ma-adopts-machine-learning-to-help-optimize-water-sustainability-projects","status":"publish","type":"post","link":"https:\/\/elmontgomery.com\/es\/hydro-notes\/ma-adopts-machine-learning-to-help-optimize-water-sustainability-projects\/","title":{"rendered":"Montgomery &amp; Associates adopta el aprendizaje autom\u00e1tico para optimizar proyectos de sostenibilidad h\u00eddrica"},"content":{"rendered":"<div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-1 fusion-flex-container nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"--awb-border-radius-top-left:0px;--awb-border-radius-top-right:0px;--awb-border-radius-bottom-right:0px;--awb-border-radius-bottom-left:0px;--awb-flex-wrap:wrap;\" ><div class=\"fusion-builder-row fusion-row fusion-flex-align-items-flex-start fusion-flex-content-wrap\" style=\"max-width:1248px;margin-left: calc(-4% \/ 2 );margin-right: calc(-4% \/ 2 );\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-0 fusion_builder_column_1_1 1_1 fusion-flex-column\" style=\"--awb-bg-blend:overlay;--awb-bg-size:cover;--awb-width-large:100%;--awb-margin-top-large:0px;--awb-spacing-right-large:1.92%;--awb-margin-bottom-large:0px;--awb-spacing-left-large:1.92%;--awb-width-medium:100%;--awb-spacing-right-medium:1.92%;--awb-spacing-left-medium:1.92%;--awb-width-small:100%;--awb-spacing-right-small:1.92%;--awb-spacing-left-small:1.92%;\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\"><div class=\"fusion-text fusion-text-1 fusion-text-no-margin\" style=\"--awb-margin-bottom:-20px;\"><h3>Planificaci\u00f3n e implementaci\u00f3n de proyectos de sostenibilidad h\u00eddrica<\/h3>\n<\/div><div class=\"fusion-text fusion-text-2\"><p class=\"body\">En el oeste de Estados Unidos, diversas agencias y proveedores de agua est\u00e1n planificando e implementando proyectos de sostenibilidad de los recursos h\u00eddricos para responder al cambio clim\u00e1tico y a la creciente demanda de suministro. En California, las Agencias de Sostenibilidad de las Aguas Subterr\u00e1neas (GSA, por sus siglas en ingl\u00e9s) deben alcanzar y mantener la sostenibilidad de las aguas subterr\u00e1neas para 2040, de conformidad con la Ley de Gesti\u00f3n Sostenible de las Aguas Subterr\u00e1neas (SGMA, por sus siglas en ingl\u00e9s). Para afrontar este reto, las GSA est\u00e1n desarrollando proyectos como la reutilizaci\u00f3n del agua, la recarga gestionada de acu\u00edferos, las transferencias de agua entre agencias y el almacenamiento y la recuperaci\u00f3n de acu\u00edferos (ASR, por sus siglas en ingl\u00e9s) para almacenar y utilizar eficazmente las aguas subterr\u00e1neas.<\/p>\n<p class=\"body\">La planificaci\u00f3n eficaz de proyectos de sostenibilidad de aguas subterr\u00e1neas que tengan en cuenta el cambio clim\u00e1tico suele requerir el uso de un modelo de flujo de aguas subterr\u00e1neas para simular los impactos de las configuraciones del proyecto con d\u00e9cadas de antelaci\u00f3n. Estas simulaciones de modelos de aguas subterr\u00e1neas pueden tardar horas o d\u00edas en ejecutarse, y la optimizaci\u00f3n adecuada de los proyectos requiere numerosas simulaciones iterativas para identificar configuraciones mejoradas. <span class=\"normaltextrun\">Para acelerar la optimizaci\u00f3n de proyectos de sostenibilidad utilizando un modelo de aguas subterr\u00e1neas, Montgomery &amp; Associates (M&amp;A) <\/span>Desarrollamos un nuevo flujo de trabajo que utiliza algoritmos de aprendizaje autom\u00e1tico (ML) para planificar, preprocesar, posprocesar y evaluar simulaciones de modelos f\u00edsicos de forma aut\u00f3noma. A este enfoque de flujo de trabajo lo denominamos Optimizaci\u00f3n Guiada por Aprendizaje Autom\u00e1tico (MLGO).<\/p>\n<\/div><div class=\"fusion-text fusion-text-3 fusion-text-no-margin\" style=\"--awb-margin-top:-20px;--awb-margin-bottom:-20px;\"><h3>Optimizaci\u00f3n guiada por aprendizaje autom\u00e1tico<\/h3>\n<\/div><div class=\"fusion-text fusion-text-4 fusion-text-no-margin\" style=\"--awb-margin-bottom:;\"><p>MLGO consiste en un proceso acoplado y automatizado donde los algoritmos de ML aprenden de las entradas y salidas de un modelo f\u00edsico de flujo de agua subterr\u00e1nea para optimizar el dise\u00f1o del proyecto y estimar combinaciones que cumplan con los objetivos de sostenibilidad. El flujo de trabajo de ML consiste en identificar autom\u00e1ticamente nuevas configuraciones y combinaciones de proyectos bas\u00e1ndose en los resultados de modelos f\u00edsicos previos, considerando los objetivos de optimizaci\u00f3n especificados por el usuario. Estos objetivos de optimizaci\u00f3n, definidos por el usuario, pueden reflejar los criterios de sostenibilidad deseados, los objetivos de viabilidad o las m\u00e9tricas de suministro. Este flujo de trabajo combina la eficiente capacidad de procesamiento de ML con la f\u00edsica real contenida en los modelos f\u00edsicos de flujo de agua subterr\u00e1nea.<\/p>\n<\/div><div class=\"fusion-text fusion-text-5\" style=\"--awb-margin-top:30px;\"><p><em><span style=\"color: #004b8d;\">La siguiente ilustraci\u00f3n describe c\u00f3mo MLGO combina algoritmos de aprendizaje autom\u00e1tico con un modelo f\u00edsico de flujo de agua subterr\u00e1nea para optimizar los sistemas.<\/span><\/em><\/p>\n<\/div><div class=\"fusion-image-element\" style=\"text-align:center;--awb-margin-bottom:20px;--awb-caption-title-font-family:var(--h2_typography-font-family);--awb-caption-title-font-weight:var(--h2_typography-font-weight);--awb-caption-title-font-style:var(--h2_typography-font-style);--awb-caption-title-size:var(--h2_typography-font-size);--awb-caption-title-transform:var(--h2_typography-text-transform);--awb-caption-title-line-height:var(--h2_typography-line-height);--awb-caption-title-letter-spacing:var(--h2_typography-letter-spacing);\"><span class=\"fusion-imageframe imageframe-none imageframe-1 hover-type-none\"><img decoding=\"async\" width=\"923\" height=\"200\" title=\"Diagrama de flujo\" src=\"https:\/\/elmontgomery.com\/wp-content\/uploads\/2025\/05\/Flowchart.jpg\" data-orig-src=\"https:\/\/elmontgomery.com\/wp-content\/uploads\/2025\/05\/Flowchart.jpg\" alt class=\"lazyload img-responsive wp-image-12868\" srcset=\"data:image\/svg+xml,%3Csvg%20xmlns%3D%27http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%27%20width%3D%27923%27%20height%3D%27200%27%20viewBox%3D%270%200%20923%20200%27%3E%3Crect%20width%3D%27923%27%20height%3D%27200%27%20fill-opacity%3D%220%22%2F%3E%3C%2Fsvg%3E\" data-srcset=\"https:\/\/elmontgomery.com\/wp-content\/uploads\/2025\/05\/Flowchart-200x43.jpg 200w, https:\/\/elmontgomery.com\/wp-content\/uploads\/2025\/05\/Flowchart-400x87.jpg 400w, https:\/\/elmontgomery.com\/wp-content\/uploads\/2025\/05\/Flowchart-600x130.jpg 600w, https:\/\/elmontgomery.com\/wp-content\/uploads\/2025\/05\/Flowchart-800x173.jpg 800w, https:\/\/elmontgomery.com\/wp-content\/uploads\/2025\/05\/Flowchart.jpg 923w\" data-sizes=\"auto\" data-orig-sizes=\"(max-width: 640px) 100vw, 923px\" \/><\/span><\/div><div class=\"fusion-text fusion-text-6 fusion-text-no-margin\" style=\"--awb-margin-bottom:-20px;\"><h3>Estudio de optimizaci\u00f3n del agua regional del condado central de Santa Cruz<\/h3>\n<\/div><div class=\"fusion-text fusion-text-7\"><p>M&amp;A utiliz\u00f3 MLGO para apoyar el Estudio Regional de Optimizaci\u00f3n del Agua del Condado Medio de Santa Cruz (Estudio). El objetivo del Estudio es respaldar la selecci\u00f3n de proyectos de suministro de agua y acciones de gesti\u00f3n dentro de la Cuenca de Aguas Subterr\u00e1neas del Condado Medio de Santa Cruz (Cuenca), con una sobreexplotaci\u00f3n cr\u00edtica, para operaciones a largo plazo y beneficios regionales compartidos, incluyendo la gesti\u00f3n sostenible de las aguas subterr\u00e1neas y las necesidades regionales de suministro de agua. Los proyectos de suministro de agua y las acciones de gesti\u00f3n considerados incluyen ASR, la recarga de agua reciclada purificada (PWS) de Pure Water Soquel y las transferencias de agua entre agencias. Cada proyecto contiene numerosos par\u00e1metros de implementaci\u00f3n, y la cantidad de posibles configuraciones es pr\u00e1cticamente ilimitada.<\/p>\n<p>Para este trabajo, MLGO recibi\u00f3 capacitaci\u00f3n sobre las entradas y salidas del modelo f\u00edsico calibrado de flujo de agua subterr\u00e1nea existente y se le asign\u00f3 la tarea de identificar configuraciones simuladas de proyectos que mejoren el suministro de agua regional, manteniendo al mismo tiempo la viabilidad y la sostenibilidad. Bajo la supervisi\u00f3n del modelador de agua subterr\u00e1nea y limitado por par\u00e1metros de implementaci\u00f3n realistas definidos por el usuario, MLGO aprendi\u00f3 de cada simulaci\u00f3n iterativa y progres\u00f3 hacia la optimizaci\u00f3n del proyecto con el tiempo. Este proceso culmin\u00f3 con la identificaci\u00f3n de cuatro alternativas robustas para la gesti\u00f3n del suministro de agua, que representan una gama de posibles inversiones en infraestructura y las mejoras asociadas al suministro de agua regional. El uso de MLGO contribuy\u00f3 a la obtenci\u00f3n de un producto final mejorado, ya que el proceso pudo avanzar de forma aut\u00f3noma hacia la optimizaci\u00f3n y simular muchas m\u00e1s configuraciones de proyecto de las que habr\u00edan sido factibles con t\u00e9cnicas de optimizaci\u00f3n manual.<\/p>\n<\/div><div class=\"fusion-text fusion-text-8\"><p><span style=\"color: #004b8d;\"><em>El siguiente esquema ilustra el flujo de trabajo general de MLGO, que progresa de forma aut\u00f3noma hasta alcanzar la optimizaci\u00f3n. <\/em><\/span><\/p>\n<\/div><div class=\"fusion-image-element\" style=\"text-align:center;--awb-caption-title-font-family:var(--h2_typography-font-family);--awb-caption-title-font-weight:var(--h2_typography-font-weight);--awb-caption-title-font-style:var(--h2_typography-font-style);--awb-caption-title-size:var(--h2_typography-font-size);--awb-caption-title-transform:var(--h2_typography-text-transform);--awb-caption-title-line-height:var(--h2_typography-line-height);--awb-caption-title-letter-spacing:var(--h2_typography-letter-spacing);\"><span class=\"fusion-imageframe imageframe-none imageframe-2 hover-type-none\"><img decoding=\"async\" width=\"800\" height=\"521\" title=\"Compendio\" src=\"https:\/\/elmontgomery.com\/wp-content\/uploads\/2025\/05\/Compendium-1.png\" data-orig-src=\"https:\/\/elmontgomery.com\/wp-content\/uploads\/2025\/05\/Compendium-1-800x521.png\" alt class=\"lazyload img-responsive wp-image-12871\" srcset=\"data:image\/svg+xml,%3Csvg%20xmlns%3D%27http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%27%20width%3D%271151%27%20height%3D%27750%27%20viewBox%3D%270%200%201151%20750%27%3E%3Crect%20width%3D%271151%27%20height%3D%27750%27%20fill-opacity%3D%220%22%2F%3E%3C%2Fsvg%3E\" data-srcset=\"https:\/\/elmontgomery.com\/wp-content\/uploads\/2025\/05\/Compendium-1-200x130.png 200w, https:\/\/elmontgomery.com\/wp-content\/uploads\/2025\/05\/Compendium-1-400x261.png 400w, https:\/\/elmontgomery.com\/wp-content\/uploads\/2025\/05\/Compendium-1-600x391.png 600w, https:\/\/elmontgomery.com\/wp-content\/uploads\/2025\/05\/Compendium-1-800x521.png 800w, https:\/\/elmontgomery.com\/wp-content\/uploads\/2025\/05\/Compendium-1.png 1151w\" data-sizes=\"auto\" data-orig-sizes=\"(max-width: 640px) 100vw, 800px\" \/><\/span><\/div><div class=\"fusion-text fusion-text-9 fusion-text-no-margin\" style=\"--awb-margin-top:60px;--awb-margin-bottom:-60px;\"><h3>Otras aplicaciones<\/h3>\n<\/div><div class=\"fusion-text fusion-text-10 fusion-text-no-margin\" style=\"--awb-margin-top:40px;--awb-margin-bottom:40px;\"><p class=\"body\" style=\"margin: 6.0pt 0in 10.0pt 0in;\">Adem\u00e1s de ayudar a las GSA con la optimizaci\u00f3n del suministro de agua y la planificaci\u00f3n de proyectos, los profesionales de fusiones y adquisiciones pueden utilizar MLGO para diversas aplicaciones de gesti\u00f3n de aguas subterr\u00e1neas en entornos de recursos h\u00eddricos, miner\u00eda y medio ambiente. Ofrecemos uno de los equipos de modelado de aguas subterr\u00e1neas m\u00e1s grandes y experimentados del oeste de Estados Unidos, con m\u00e1s de 20 profesionales expertos en diversos c\u00f3digos num\u00e9ricos de flujo y transporte de aguas subterr\u00e1neas. Nuestros modelos predictivos pueden incorporar incertidumbres futuras, como el cambio clim\u00e1tico, o la estimaci\u00f3n de la demanda futura de agua urbana y agr\u00edcola mediante escenarios o simulaciones probabil\u00edsticas. A menudo utilizamos modelos geol\u00f3gicos 3D para generar datos de entrada y presentar los resultados de forma accesible para las partes interesadas del proyecto. Nuestros expertos en SGMA son expertos en comunicar resultados de modelado complejos a las GSA y a las partes interesadas para apoyar la toma de decisiones a largo plazo para la gesti\u00f3n sostenible de las aguas subterr\u00e1neas.<\/p>\n<\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"align-self: center;margin-left: auto;margin-right: auto;margin-top:10px;margin-bottom:40px;width:100%;\"><div class=\"fusion-separator-border sep-shadow\" style=\"--awb-height:20px;--awb-amount:20px;background:radial-gradient(ellipse at 50% -50% , var(--awb-color6) 0px, rgba(255, 255, 255, 0) 80%) repeat scroll 0 0 rgba(0, 0, 0, 0);background:-webkit-radial-gradient(ellipse at 50% -50% , var(--awb-color6) 0px, rgba(255, 255, 255, 0) 80%) repeat scroll 0 0 rgba(0, 0, 0, 0);background:-moz-radial-gradient(ellipse at 50% -50% , var(--awb-color6) 0px, rgba(255, 255, 255, 0) 80%) repeat scroll 0 0 rgba(0, 0, 0, 0);background:-o-radial-gradient(ellipse at 50% -50% , var(--awb-color6) 0px, rgba(255, 255, 255, 0) 80%) repeat scroll 0 0 rgba(0, 0, 0, 0);\"><\/div><\/div><div class=\"fusion-text fusion-text-11\"><h4 class=\"body fusion-responsive-typography-calculated\" style=\"--fontsize: 27; line-height: 1.2;\" data-fontsize=\"27\" data-lineheight=\"32.4px\"><b>Sobre el Autor<\/b><\/h4>\n<p class=\"body\"><i><a href=\"https:\/\/elmontgomery.com\/staff-bio\/patrick-wickham\/\" data-no-translation=\"\">Patrick Wickham, P.G.<\/a><\/i><i>, es hidroge\u00f3logo en la oficina de M&amp;A en Pasadena y se especializa en modelado de aguas subterr\u00e1neas y aplicaciones de aprendizaje autom\u00e1tico. Patrick participar\u00e1 en el <\/i><a href=\"https:\/\/sites.google.com\/view\/2025-hydroml-symposium\/home\" target=\"_blank\" rel=\"noopener\" data-no-translation=\"\"><i>HydroML 2025 Symposium<\/i><\/a><i>, organizado por la Universidad de California-Irvine del 27 al 29 de mayo. <\/i><\/p>\n<\/div><\/div><\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"","protected":false},"author":4,"featured_media":12889,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-12866","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-hydro-notes"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Montgomery &amp; Associates Adopts Machine Learning to Help Optimize Water Sustainability Projects - Montgomery &amp; Associates<\/title>\n<meta name=\"description\" content=\"M&amp;A Adopts Machine Learning to Help Optimize Water Sustainability Projects\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/elmontgomery.com\/es\/hydro-notes\/ma-adopts-machine-learning-to-help-optimize-water-sustainability-projects\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Montgomery &amp; 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