Potenciales fondos:
- https://www.theatlascapital.com/
- El de Hugo Bahamon
Servicios intermedios de sostenibilidad
- Reporte de cumplimiento en Bolsa de Valores
Pólizas Climáticas con soluciones basadas en la naturaleza y en la IA: Créditos Verdes para Captura de Carbono o Reducción de Emisión vía Transición Energética a Neutralidad Carbono en 2050 (Blockchain NEAR neutro en carbono: Stack Python Rust) Seguro Climático con prima dinámica de corresponsabilidad con sistemas inteligentes de: Monitorear + Mitigar-Reaccionar (kit de auxilios) + Adaptar-Prevenir (kit flora + kit fauna)
- Vientos: https://towardsdatascience.com/time-series-for-climate-change-forecasting-wind-power-8ed6d653a255
- Granizo
- Lluvia: https://towardsdatascience.com/does-rain-predict-rain-us-weather-data-and-the-correlation-of-rain-today-and-tomorrow-3a62eda6f7f7 https://geosen.medium.com/extracting-and-visualising-rainfall-data-to-excel-28530169f113 WATCHING FROM SPACE: https://towardsdatascience.com/watching-storms-from-space-a-python-script-for-creating-an-amazing-view-79d8bb2f5ff1
- Sequía: https://eroglumit.medium.com/monitoring-groundwater-with-geodesy-b3ecd0f91d57 https://towardsdatascience.com/new-data-demonstrates-that-2023-was-the-hottest-summer-ever-d92d500a8f01
- Inundaciones: https://github.com/keanteng/flood_risk_model https://khorkeanteng.medium.com/a-streamlit-app-for-flood-analysis-b42020bc9048 https://khorkeanteng.medium.com/creating-chatfloodrisk-dd39fabdda32 https://khorkeanteng.medium.com/flood-risk-modeling-with-logistic-regression-167840cf1036 https://medium.com/sentinel-hub/the-use-of-satellite-imagery-in-crisis-management-after-flooding-382be517224f
- Heladas truenos: supuestamente caen 50 truenos cada segundo en todo el planeta
- Deslizamientos
- Avalanchas
- Terremotos
- Huracanes: https://huggingface.co/spaces/ahuang11/hurdat_tracks_viewer https://blog.stackademic.com/transform-a-python-script-into-an-interactive-web-app-and-make-it-performant-73fa3b304cdf
- incendios: https://towardsdatascience.com/satellites-can-see-invisible-lava-flows-and-active-wildires-but-how-python-371915464d1c https://python.plainenglish.io/collect-ndvi-data-for-every-us-wildfire-in-2022-using-python-and-google-earth-engine-77dfa53ac0c3 https://elpais.com/clima-y-medio-ambiente/2023-08-25/que-deberiamos-aprender-de-los-incendios-de-hawai.html https://www.mauicounty.gov/DocumentCenter/View/129491/Report-on-Wildfire-Prevention--Cost-Recovery-on-Maui---Part-4-Exhibit-D-25-MB https://www.kaggle.com/code/edhirif/predict-the-causes-of-wildfires-using-python https://towardsdatascience.com/how-to-create-your-own-cv-dataset-using-satellite-imagery-wildfires-from-space-8295c0cca028
- Océanos 12.1 Temperatura https://ai.gopubby.com/spatial-downscaling-of-2-meter-air-temperature-using-random-forest-on-google-earth-engine-gee-6b4093954d1d + 12.2 Hielo polar: https://towardsdatascience.com/make-beautiful-and-useful-spaghetti-plots-with-python-ec4269d7e8c9
Historic Data: 180 countries, 39.000 weather stations: https://medium.com/geekculture/creating-the-biggest-climate-temperature-dataset-9136cafa4298 https://medium.com/@alexroz/weather-and-climate-data-for-a-data-science-project-where-to-take-df6eb118a46d https://medium.com/@mahyar.aboutalebi/how-to-download-process-and-visualize-climate-data-in-python-cfd6d8350322
3D Rain visualization https://studio.foursquare.com/public/05fb384f-e3e2-472f-b9ab-914a74f3adc5
Data Monitoring: https://adamplatform.eu/home-page/ https://adamplatform.eu/
Weather by seasons: https://openweathermap.medium.com/understanding-the-four-seasons-9d641ebef207
Weather - Climate Dissaster Forecast: https://www.kaggle.com/code/thuongtuandang/weather-prediction-with-mixture-models-88 based on historical data (temperature, precipitacy, humidity and wind speed) Weather Labels Climate dissaster Labels Model: Decision Tree Classifier + Naive Bayes Classifier (Multinomial, Gaussian and Bernoulli's) full forecast: (dataframe: weekday, forecast, temperature, humidity and wind speed) reference: youtube AK Python real time data: intensity, duration, extent of Precipitation
(IFC: International Fire Consultants Ltd: Certificado para Financiación) https://www.ifccertification.com/clients/application/default.html Manufacturer Installer Alarm Systems + Ductwork + Fire Doors + Glazed Screens
- Water + Steel ISO 9001 Quality Regulator
SAles Impact. Wallmart`s Case https://www.kaggle.com/c/walmart-recruiting-sales-in-stormy-weather
Geospatial Count of Trees: https://towardsdatascience.com/speed-up-your-geospatial-data-analysis-with-r-trees-4f75abdc6025
Potenciales Aliados: https://www.mapfre.com.co/seguros-agropecuarios/climatico/ https://www.arbol.io/ https://agroseguro.es/
Referentes: https://climate-sci-graph.streamlit.app/ GeoSpatial Solutions: https://blog.gishub.org/ https://github.com/opengeos https://github.com/giswqs/giswqs Atento prevención y reacción: https://weather-chat-ai.fly.dev/ Blockchain-Tokens-Support: https://greentoken.org/ https://quantoz.com/publications/tokenization-bringing-liquidity-to-the-green-bonds-market/ Ejemplo de predicción bajo ciertos parámetros: https://alejandro-ao-streamlit-cancer-predict-appmain-uitjy1.streamlit.app/
Global Population Density for 400m H3: https://data.humdata.org/dataset/kontur-population-dataset
Colombia a 2050: https://openknowledge.worldbank.org/entities/publication/9b706816-2618-48d0-87d4-e7e99b7ad779 Colombia Riesgos de Destastres por Municipios: https://colaboracion.dnp.gov.co/CDT/Prensa/IndiceMunicipaldeRiesgodeDesastres.pdf
Referentes: https://www.undrr.org/comprehensive-disaster-and-climate-risk-management-crm https://www.worldbank.org/en/results/2017/12/01/climate-insurance https://www.mckinsey.com/industries/financial-services/our-insights/capturing-the-climate-opportunity-in-insurance https://indexinsuranceforum.org/climate-insurance Data USA: https://www.weather.gov/ GeoReports: https://medium.com/analytics-vidhya/python-for-geosciences-scatter-plots-and-pdf-reports-4e4dcec70e4d Water Detection: https://towardsdatascience.com/water-detection-in-high-resolution-satellite-images-using-the-waterdetect-python-package-7c5a031e3d16
Addressing Climate Change - The Rockefeller Foundation https://www.rockefellerfoundation.org/bellagio-center/convenings/addressing-climate-change/ https://www.adb.org/publications/blockchain-tokenized-securities-potential-green-finance
Time Series for Climate Change: Forecasting Energy Demand | by Vitor Cerqueira | May, 2023 | Towards Data Science https://towardsdatascience.com/time-series-for-climate-change-forecasting-energy-demand-79f39c24c85e
Data Mesh: Making Climate Data Easy to Find, Use, and Share | by Eric Broda | Towards Data Science https://towardsdatascience.com/making-climate-data-easy-to-find-use-and-share-5190a0926407
Precipitation & South America`s Raster Data: https://towardsdatascience.com/harnessing-precipitation-and-climatological-raster-data-in-south-america-18ec36d683
Historicos: Argentina 2022-2023 sequias ompacto 20.000MUSD fuente DW Colombia Fenómeno del Niño + Armero:
Certification in Climate Risk: https://www.cisi.org/cisiweb2/cisi-website/study-with-us/certificate-in-climate-risk
Monitoring Sea Surface Temperature at the global level with GEE | by Bryan R. Vallejo | Mar, 2023 | Towards Data Science https://towardsdatascience.com/monitoring-sea-surface-temperature-at-the-global-level-with-gee-1d7349c7da6
Mitigar-Socorrer: https://es.weforum.org/agenda/2023/02/terremoto-en-turquia-y-siria-como-la-inteligencia-artificial-y-las-nuevas-tecnologias-ayudan-en-las-labores-de-socorro/
Python 3D Lidar https://towardsdatascience.com/3d-python-workflows-for-lidar-point-clouds-100ff40e4ff0
JavaScript 3D + ChatGPT: https://levelup.gitconnected.com/train-chatgpt-to-automate-3d-web-gis-development-1217aaf155c8
NVIDIA omniverse: Industrial Metaverse https://cesium.com/blog/2023/03/21/cesium-for-omniverse-launch/
HeatMap: https://dwikita-ichsana.medium.com/meteorology-101-how-to-plot-heatmap-953688ac4870
Rol Aliado:
- Smart Contracts in Solidity with MetaMask wallet
- User Portal
- FrontEnd - JS - ThreeJS
Tareas feb 2023 Compartir bibliografía de las bases Fechas Licencias Formatos… Seguir avanzando código demo Consulta Ibagué Santander Ciudad de México: finales de Marzo lanzando Miami: ITC: 24-26 de abril
Streamlit Visualizaciones: Clima: General Widgets: 1) Elements, 2) Vertical Slider, 3) Toggle Swithc, 4) Custom Toggle. Clima: Charts. Echarts: 1) Graph, 2) Graph Force, 3) Liquidfill, 4) Gauge Ring, 5) Theme River, 6) Legent Event, 7) SunBurst Clima: Pyechart: 1) map, 2) Prophet library https://link.medium.com/7XyZWcwKgyb
Visualization https://flourish.studio/examples/ Conections globe
Sensores de Rayos: Kreaunos LATAM www.keraunos.co 52 millones el sensor de campo electromagnético para 25km2 Servicio mensual de soporte Daniela Ramírez: 3125315092 Cobertrura Colombia, Panamá, México, Ecuador y Perú. Ideam intercambia datos
Satellite Image Time Series: https://towardsdatascience.com/what-is-a-satellite-image-time-series-c0516c534ba9 Agriculture, Forest Monitoring, Climate, Urban Planning, Disaster Response, SITS Carbon Dioxide: https://medium.datadriveninvestor.com/how-to-estimate-carbon-dioxide-with-satellite-data-using-geoap-c76813b647da
Three Js referentes: https://threejs.org/examples/#misc_controls_fly https://threejs.org/examples/#css2d_label
Seguros Predicción de Reclamos https://github.com/fardil-b/Insurance-Claims-Severity-Prediction
3D Wheater https://www.youtube.com/watch?v=ylkbw3-OSYA
3dRayo https://threejs.org/examples/#webgl_lightningstrike
3dNube https://threejs.org/examples/#webgl2_volume_cloud
3dAgua https://threejs.org/examples/#webgl_gpgpu_water
GIS with ChatGpt https://medium.com/@moradouasti/the-future-of-gis-with-chatgpt-a9d2588e841a https://medium.com/data-and-beyond/dash-ing-maps-with-chatgpt-and-python-interactive-maps-in-no-time-flat-bf418566b71d
Data: GLOBAL https://medium.com/codex/realtime-data-scraping-with-python-517bf5a5eb84 https://towardsdatascience.com/the-most-powerful-climate-data-remains-hidden-87cb0a0bf302 https://towardsdatascience.com/co2-emissions-infographics-in-python-369dd968eb84 https://towardsdatascience.com/top-5-places-to-find-climate-change-datasets-e3f5a7ee2139 WILDFIRES: AEMET, Modis Collection 6.1 by Nasa Terra and Aqua Satellites global fire data, Spains Miteco
Mando Mapa: https://medium.com/@insightsbees/streamlit-for-dashboards-and-web-apps-a-complete-beginners-tutorial-part-2-ee6e9fe8e510 https://towardsdatascience.com/streamlit-from-scratch-build-a-data-dashboard-with-streamlits-layout-and-ui-features-a2fc2f0a6a59
Demo Mundial: https://github.com/initze/noaaplotter_streamlit Co2Emissions https://towardsdatascience.com/an-interactive-co2-emissions-dashboard-with-plotly-and-streamlit-b0bd4ae80cc8 Supply Chain CO2 Emissions: https://s-saci95.medium.com/green-inventory-management-case-study-790f2ed7ef7e
Semantic Segmentation: buildings, roads... https://medium.com/@jrballesteros/remote-sensing-datasets-for-artificial-intelligence-semantic-segmentation-1737ca1f35c1
WildFires Analysis: wildfires in the United States from 1992 to 2015: https://levelup.gitconnected.com/unlock-the-potential-of-data-visualization-with-these-amazing-python-libraries-1551f6505a29 https://towardsdatascience.com/using-artificial-intelligence-to-predict-the-spread-of-wildfires-with-python-30386e28162f https://github.com/ismael-araujo/Analyzing-Wildfires-in-Brazil https://www.kaggle.com/code/edhirif/predict-the-causes-of-wildfires-using-python https://medium.com/data-and-beyond/dash-ing-maps-with-chatgpt-and-python-interactive-maps-in-no-time-flat-bf418566b71d
Servicio Satelital en Tiempo Real con AWS: https://affine.ai/telescope/ https://affine.medium.com/see-the-world-through-your-lens-introducing-next-gen-ai-satellite-image-segmentation-solution-a236dda7cf17
Historical Climate Change: https://towardsdatascience.com/visualizing-climate-change-a-step-by-step-guide-to-reproduce-climate-stripes-with-python-ea1d440e8e8d
Real time Weather Updates: https://medium.com/codex/realtime-data-scraping-with-python-517bf5a5eb84 https://github.com/gerardrbentley/peak-weather https://gerardrbentley-peak-weather-streamlit-appstreamlit-app-6asgew.streamlit.app/ Meteostat API: https://medium.com/@shantanu.dave01/extracting-and-analyzing-weather-data-using-meteostat-api-238d67de8e98
Time Series for Climate Change: Forecasting Wind Power | by Vitor Cerqueira | Mar, 2023 | Towards Data Science https://towardsdatascience.com/time-series-for-climate-change-forecasting-wind-power-8ed6d653a255
Referentes: https://sharmaji27.medium.com/live-weather-forecast-flask-app-ffcbbc4d97a7
Problema
Madagascar hambre por clima
46 muertos en junio 2021 por inundaciones en Alemania
+100 muertos en julio 2021 por inundaciones en Alemania
En 2050 Santa Marta y Cartagena bajo el agua
Categorías de los huracanes
+500 muertos ola de calor en españa 2022
Desorden en las estaciones
Primavera está iniciando casi un mes antes
Países desarrollados responsables del 92% emisiones
El calor mata más Estadounidenses que cualquier otro evento climático extremo
National Weather Service
Construcciones de vivienda en zonas
Cerca a río
Relleno inestable
Borde de acantilado
Zonas bajas de quebradas
Zonas de mucha pendiente
Polución en el Aire es el mayor riesgo de salud pública en Europa
40.400 muertes prematuras por Dióxido de Nitrógeno (NO2)
Soluciones
Sponge City en China. Against flooding
Lluvias artificiales y siembra de nubes en Emiratos Árabes Unidos
EkoMuros
Botellas de plástico que almacenan agua lluvia
Se han instalado más de 250 ekomuros
Empleo local para instalar y mantener
Manuales de uso
3.000 litros de agua al mes
Beneficia a 65.000 personas
2020 global energy award
Más fauna estratégica equilibrio ecosistema
Rios:
Manatís
Corpamag
Plantas
Abejas
Sombrillas Gigantes en Plazas
German Company SL Rasch
250 sombrillas en Hiyaz, Arabia Saudí
Hasta 8 grados centígrados menos
Cortinas Forestales
Contra inundaciones, erosiones, viento veloz, conservar la humedad
Microalgae:
absorven carbon y polución del agua
Fuente de proteína, alternativa a la carne y a la soya
Pintar de blanco para el calor extremo: 2017 Los Angeles
5 grados centígrados menos
Monitorear
www.adamplatform.eu
Estrés hídrico:
Demanda de Agua > Oferta de Agua
Reservas de Agua -> Riesgo Sequía
Incendios
Open Weather Map tutorial
https://github.com/hicodersofficial/weather-app
Rain Fall Prediction
Github: Vatshayan
Satellite Imagery
www.askpython.com
Examples Satellite
Open Space Project
Copernicus Sentinel-2 (lluvia y reservas de agua)
Copernicus Sentinel-5P (Aire)
Copernicus Sentinel-3 (Sea and Land Surface Temperature)
3D: Tyler Morgan Wall
World Risk Index
Nexos + 1
European State of Climate
Conciencia
Picturing Our Future: 184 lugares emblemáticos con 3 grados adicionales
Limpiar océanos
Bodegas de seguridad
Capturar
Tormentas eléctricas
Diferenciar
Relámpago: luz que desprende el rayo
Rayo: fenómeno natural de descarga eléctrica: 200.000 Km por hora
Trueno: sonido emitido por el rayo
Fuerza de Huracanes
Consejos ante huracanes categoría 5
1. Cuida la batería de tu celular
2. Consigue baterías adicionales para tus aparatos electrónicos
3. Compra un generador de energía
4. GPS por si toca evacuar
Bolas anti incendios (Elide Fire extinguishing ball)
Leer las nubes
Buen tiempo
Cirrus: delgadas
Cúmulos: algodonosas
Altocúmulos: globulares
Mal tiempo
Altostratus
Cumulonimbus: grandes y altas, relámpagos y lluvia
Nimbustratus: gris oscuro, lluvia por muchas horas pero no suele ser intenso
Fuentes de datos: -)Open Weather -) tutiempo.net -) Mexico: https://climateknowledgeportal.worldbank.org/country/mexico/vulnerability
Referentes Mapas -) Python - Streamlit: https://docs.streamlit.io/library/api-reference/charts/st.pydeck_chart -) R - Rayder: www.rayrender.net
Time Series for Climate Change: Forecasting Large Ocean Waves | by Vitor Cerqueira | Apr, 2023 | Towards Data Science https://towardsdatascience.com/time-series-for-climate-change-forecasting-large-ocean-waves-78484536be36
Novus Agro + Novus Aqua + Novus Clima: riego inteligente Tesis IA: Definición de Problema Lean Canvas Capacidades IA Viabilidad Económica CAPEX + OPEX: 166KUSD-277KUSD Anexo 1 y 2: novus agro Necesidad: sistema de detección de evapotranspiración Datos finales tesis IA sistema de riego inteligente: En síntesis los datos requeridos para procesar estos cálculos son: Ubicación física temporal: Latitud – Longitud – Altitud - Temporalidad Clima: temperatura, humedad, radiación solar, velocidad del viento, precipitaciones, lluvia útil (PE), presión. Cultivo: coeficiente cultivo (Kc), ciclo, sombra, conductividad, anegamiento, humedad follaje, nutrientes requeridos. Evapotranspiración (ET): referencia y cultivo (ET*Kc) Riego: Eficiencia de riego (EA): gravedad, goteo, aspersión y/o adaptable.