Pronóstico de pérdidas agrícolas por sequía en México usando técnicas de Aprendizaje Automático

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Síntesis de proyecto de investigación doctoral que tiene el objetivo de pronosticar impactos de las sequías en la agricultura de temporal en México, a escala de Distrito de Desarrollo Rural, usando técnicas de Aprendizaje Automático tomando como caso de estudio al cultivo de maíz grano (Zea Mays L.).

Pronóstico de pérdidas agrícolas por sequía en
México usando técnicas de Aprendizaje Automático
Roberto A. Real Rangel | Candidato a Doctor en Ingeniería
Instituto de Ingeniería
Programa de Maestría y Doctorado en Ingeniería
Universidad Nacional Autónoma de México
2º Seminario Virtual Diáspora Hídrica: Jóvenes Mexicanos Explorando las Fronteras
del Conocimiento del Agua
3 al 6 de agosto de 2021
1
Ingeniería

Las sequías y sus impactos 2
Eventos naturales temporales y recurrentes que se manifiestan
con la disminución de la disponibilidad natural de agua.
Impactos
Agricultura de temporal
Sector más directamente
afectado por las sequías
49% 51%
Maíz grano (Zea mays L.)
Producción en México en
2020

Objetivos y alcances
Pronosticar los impactos agrícolas
de las sequías en México
desde un enfoque estadístico, usando técnicas
de Aprendizaje Automático.
Reducción del
rendimiento (masa
producida/superficie
sembrada)
Maíz grano (Zea Mays L.)
Agricultura de temporal
Ciclo Primavera-Verano
Tipo de impacto Caso de estudio
3

Área y diseño del estudio 5
Recopilar información
Control de calidad y
corrección de sesgo
Modelo de pronóstico de
rendimiento agrícola
Variación del rendimiento
Identificar pérdidas por
sequía
Reportar
Procedimiento
Área de estudio: todo el país, a escala de DDR
0 990 K
98 K
39 K
14 K
2 K
Producción (ton)
Producción de maíz grano por Distrito de Desarrollo
Rural en México en 2020
20°N
30°N
100°O 90°O
Golfo de
México
Océano Pacífico
N

Aplicación de aprendizaje supervisado 7
Precipitación, temperatura,
índices de salud vegetal, etc.,
durante el ciclo agrícola
Atributos de entrada (𝐗)
(2006-2019)
Proceso de producción
agrícola
f(X)
Rendimiento efectivo de maíz al
final del ciclo agrícola
Respuesta (𝐲)
(2006-2019)
Bosque de Regresión
Cuantílica
Aproximación መ
𝑓 𝐗
Observaciones conocidas Sistema modelado Observaciones conocidas
Nuevas (futuras)
observaciones
Respuesta
estimada (ො
𝐲)
Algoritmo de Aprendizaje Automático

0%
100%
Pronóstico de rendimiento de maíz 9
-6 meses -3 meses
-9 meses
-12 meses
Abr May Jun Jul Ago Sep Oct Nov Dic Ene Feb Mar
Periodo de prueba: 2017-2019
Sup. sembrada
Producción
unidades
(kg/ha)
MAE = 353.00
MAE = 439.80
MAE = 463.01
MAE = 458.05

0%
100%
Pronóstico de variación del rendimiento de maíz 10
-6 meses -3 meses
-9 meses
-12 meses
Abr May Jun Jul Ago Sep Oct Nov Dic Ene Feb Mar
MAE = 250.87
Periodo de prueba: 2017-2019
Sup. sembrada
Producción
unidades
(kg/ha)
MAE = 388.17
MAE = 400.11
MAE = 378.08

0%
100%
Pronóstico de variación del rendimiento de maíz 12
-6 meses -3 meses
-9 meses
-12 meses
Abr May Jun Jul Ago Sep Oct Nov Dic Ene Feb Mar
Periodo de prueba: 2017-2019
Sup. sembrada
Producción
unidades
(kg/ha)
Ejemplo
DDR 65
Zapopan
(Jalisco)

Comentarios finales
• El marco de trabajo propuesto, puede ser de ayuda en la toma de
decisiones bajo incertidumbre frente a una amenaza de origen
hidroclimatológico (déficit o exceso de precipitación) en el sector agrícola.
• Se ha logrado hacer pronóstico de la variación del rendimiento agrícola
usando información y herramientas públicas y de acceso gratuito.
• La precisión de los resultados se ve drásticamente reducida a partir de 6
meses de previsión. Se evaluará la precisión con 4 y 5 meses de previsión.
• Queda pendiente asociar las pérdidas a periodos de déficit (o exceso) de
agua.
13

14
Agradecimiento a los asesores
Dr. Adrián Pedrozo Acuña | II-UNAM / IMTA
Dr. J. Agustín Breña Naranjo | II-UNAM / IMTA
Dr. Luis Brito Castillo | CIBNOR
Dr. Ramón Domínguez Mora | II-UNAM
Dr. Óscar A. Fuentes Mariles | II-UNAM

15
¡Muchas gracias por su atención!
Roberto A. Real Rangel
rrealr@iingen.unam.mx
@rrealrangel
roberto-real-rangel
r.realrangel@gmail.com
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学校原件一模一样【微信：741003700 】《(lboro毕业证书)拉夫堡大学毕业证学历证书》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。本公司拥有海外各大学样板无数，能完美还原。 1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700 【主营项目】一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！二.真实使馆公证(即留学回国人员证明,不成功不收费) 三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度) 如果您处于以下几种情况： ◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】 ◇面对父母的压力，希望尽快拿到； ◇不清楚认证流程以及材料该如何准备； ◇回国时间很长，忘记办理； ◇回国马上就要找工作，办给用人单位看； ◇企事业单位必须要求办理的 ◇需要报考公务员、购买免税车、落转户口 ◇申请留学生创业基金留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才

Peatlands of Latin America and the Caribbean

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Improving the Management of Peatlands and the Capacities of Stakeholders in I...

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Climate Change All over the World .pptx

sairaanwer024

Climate change refers to significant and lasting changes in the average weather patterns over periods ranging from decades to millions of years. It encompasses both global warming driven by human emissions of greenhouse gases and the resulting large-scale shifts in weather patterns. While climate change is a natural phenomenon, human activities, particularly since the Industrial Revolution, have accelerated its pace and intensity

Evolving Lifecycles with High Resolution Site Characterization (HRSC) and 3-D...

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The incorporation of a 3DCSM and completion of HRSC provided a tool for enhanced, data-driven, decisions to support a change in remediation closure strategies. Currently, an approved pilot study has been obtained to shut-down the remediation systems (ISCO, P&T) and conduct a hydraulic study under non-pumping conditions. A separate micro-biological bench scale treatability study was competed that yielded positive results for an emerging innovative technology. As a result, a field pilot study has commenced with results expected in nine-twelve months. With the results of the hydraulic study, field pilot studies and an updated risk assessment leading site monitoring optimization cost lifecycle savings upwards of $15MM towards an alternatively evolved best available technology remediation closure strategy.

Optimizing Post Remediation Groundwater Performance with Enhanced Microbiolog...

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Results of geophysics and pneumatic injection pilot tests during 2003 – 2007 yielded significant positive results for injection delivery design and contaminant mass treatment, resulting in permanent shut-down of an existing groundwater Pump & Treat system. Accessible source areas were subsequently removed (2011) by soil excavation and treated with the placement of Emulsified Vegetable Oil EVO and zero-valent iron ZVI to accelerate treatment of impacted groundwater in overburden and weathered fractured bedrock. Post pilot test and post remediation groundwater monitoring has included analyses of CVOCs, organic fatty acids, dissolved gases and QuantArray® -Chlor to quantify key microorganisms (e.g., Dehalococcoides, Dehalobacter, etc.) and functional genes (e.g., vinyl chloride reductase, methane monooxygenase, etc.) to assess potential for reductive dechlorination and aerobic cometabolism of CVOCs. In 2022, the first commercial application of MetaArray™ was performed at the site. MetaArray™ utilizes statistical analysis, such as principal component analysis and multivariate analysis to provide evidence that reductive dechlorination is active or even that it is slowing. This creates actionable data allowing users to save money by making important site management decisions earlier. The results of the MetaArray™ analysis’ support vector machine (SVM) identified groundwater monitoring wells with a 80% confidence that were characterized as either Limited for Reductive Decholorination or had a High Reductive Reduction Dechlorination potential. The results of MetaArray™ will be used to further optimize the site’s post remediation monitoring program for monitored natural attenuation.

ENVIRONMENT~ Renewable Energy Sources and their future prospects.

tiwarimanvi3129

This presentation is for us to know that how our Environment need Attention for protection of our natural resources which are depleted day by day that's why we need to take time and shift our attention to renewable energy sources instead of non-renewable sources which are better and Eco-friendly for our environment. these renewable energy sources are so helpful for our planet and for every living organism which depends on environment.

world-environment-day-2024-240601103559-14f4c0b4.pptx

mfasna35

Promoting Multilateral Cooperation for Sustainable Peatland management

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Global Climate Change and global warming

ballkicker20

Improving the viability of probiotics by encapsulation methods for developmen...

Open Access Research Paper

The popularity of functional foods among scientists and common people has been increasing day by day. Awareness and modernization make the consumer think better regarding food and nutrition. Now a day’s individual knows very well about the relation between food consumption and disease prevalence. Humans have a diversity of microbes in the gut that together form the gut microflora. Probiotics are the health-promoting live microbial cells improve host health through gut and brain connection and fighting against harmful bacteria. Bifidobacterium and Lactobacillus are the two bacterial genera which are considered to be probiotic. These good bacteria are facing challenges of viability. There are so many factors such as sensitivity to heat, pH, acidity, osmotic effect, mechanical shear, chemical components, freezing and storage time as well which affects the viability of probiotics in the dairy food matrix as well as in the gut. Multiple efforts have been done in the past and ongoing in present for these beneficial microbial population stability until their destination in the gut. One of a useful technique known as microencapsulation makes the probiotic effective in the diversified conditions and maintain these microbe’s community to the optimum level for achieving targeted benefits. Dairy products are found to be an ideal vehicle for probiotic incorporation. It has been seen that the encapsulated microbial cells show higher viability than the free cells in different processing and storage conditions as well as against bile salts in the gut. They make the food functional when incorporated, without affecting the product sensory characteristics.

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原版制作(Newcastle毕业证书)纽卡斯尔大学毕业证在读证明一模一样

Wildlife-AnIntroduction.pdf so that you know more about our environment

在线办理(lboro毕业证书)拉夫堡大学毕业证学历证书一模一样

Peatlands of Latin America and the Caribbean

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ENVIRONMENT~ Renewable Energy Sources and their future prospects.

world-environment-day-2024-240601103559-14f4c0b4.pptx

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Pronóstico de pérdidas agrícolas por sequía en México usando técnicas de Aprendizaje Automático

1. Pronóstico de pérdidas agrícolas por sequía en México usando técnicas de Aprendizaje Automático Roberto A. Real Rangel | Candidato a Doctor en Ingeniería Instituto de Ingeniería Programa de Maestría y Doctorado en Ingeniería Universidad Nacional Autónoma de México 2º Seminario Virtual Diáspora Hídrica: Jóvenes Mexicanos Explorando las Fronteras del Conocimiento del Agua 3 al 6 de agosto de 2021 1 Ingeniería

2. Las sequías y sus impactos 2 Eventos naturales temporales y recurrentes que se manifiestan con la disminución de la disponibilidad natural de agua. Impactos Agricultura de temporal Sector más directamente afectado por las sequías 49% 51% Maíz grano (Zea mays L.) Producción en México en 2020

3. Objetivos y alcances Pronosticar los impactos agrícolas de las sequías en México desde un enfoque estadístico, usando técnicas de Aprendizaje Automático. Reducción del rendimiento (masa producida/superficie sembrada) Maíz grano (Zea Mays L.) Agricultura de temporal Ciclo Primavera-Verano Tipo de impacto Caso de estudio 3

4. Área y diseño del estudio 5 Recopilar información Control de calidad y corrección de sesgo Modelo de pronóstico de rendimiento agrícola Variación del rendimiento Identificar pérdidas por sequía Reportar Procedimiento Área de estudio: todo el país, a escala de DDR 0 990 K 98 K 39 K 14 K 2 K Producción (ton) Producción de maíz grano por Distrito de Desarrollo Rural en México en 2020 20°N 30°N 100°O 90°O Golfo de México Océano Pacífico N

5. Aplicación de aprendizaje supervisado 7 Precipitación, temperatura, índices de salud vegetal, etc., durante el ciclo agrícola Atributos de entrada (𝐗) (2006-2019) Proceso de producción agrícola f(X) Rendimiento efectivo de maíz al final del ciclo agrícola Respuesta (𝐲) (2006-2019) Bosque de Regresión Cuantílica Aproximación መ 𝑓 𝐗 Observaciones conocidas Sistema modelado Observaciones conocidas Nuevas (futuras) observaciones Respuesta estimada (ො 𝐲) Algoritmo de Aprendizaje Automático

6. 0% 100% Pronóstico de rendimiento de maíz 9 -6 meses -3 meses -9 meses -12 meses Abr May Jun Jul Ago Sep Oct Nov Dic Ene Feb Mar Periodo de prueba: 2017-2019 Sup. sembrada Producción unidades (kg/ha) MAE = 353.00 MAE = 439.80 MAE = 463.01 MAE = 458.05

7. 0% 100% Pronóstico de variación del rendimiento de maíz 10 -6 meses -3 meses -9 meses -12 meses Abr May Jun Jul Ago Sep Oct Nov Dic Ene Feb Mar MAE = 250.87 Periodo de prueba: 2017-2019 Sup. sembrada Producción unidades (kg/ha) MAE = 388.17 MAE = 400.11 MAE = 378.08

8. 0% 100% Pronóstico de variación del rendimiento de maíz 12 -6 meses -3 meses -9 meses -12 meses Abr May Jun Jul Ago Sep Oct Nov Dic Ene Feb Mar Periodo de prueba: 2017-2019 Sup. sembrada Producción unidades (kg/ha) Ejemplo DDR 65 Zapopan (Jalisco)

9. Comentarios finales • El marco de trabajo propuesto, puede ser de ayuda en la toma de decisiones bajo incertidumbre frente a una amenaza de origen hidroclimatológico (déficit o exceso de precipitación) en el sector agrícola. • Se ha logrado hacer pronóstico de la variación del rendimiento agrícola usando información y herramientas públicas y de acceso gratuito. • La precisión de los resultados se ve drásticamente reducida a partir de 6 meses de previsión. Se evaluará la precisión con 4 y 5 meses de previsión. • Queda pendiente asociar las pérdidas a periodos de déficit (o exceso) de agua. 13

11. 15 ¡Muchas gracias por su atención! Roberto A. Real Rangel rrealr@iingen.unam.mx @rrealrangel roberto-real-rangel r.realrangel@gmail.com Pronóstico de pérdidas agrícolas por sequía en México usando técnicas de Aprendizaje Automático Ingeniería

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