Intelligent system for modeling hydrological, climatic and environmental processes
Concept
WOOD AI— is an intelligent analytics platform that is the central element of the ecosystemDREVO Living MountainsIt combines data from sensors, drones, satellites, robotic systems, and a digital twin to model natural processes, predict changes, and support decision making.
Unlike traditional monitoring systems, DREVO AI not only records current events but also models possible futures, assessing the impact of engineering solutions, climate change, and ecosystem restoration.
The main principle:
Understand the future state of the ecosystem before restoration work begins.
Main tasks
DREVO AI provides:
modeling of hydrological processes;
climate process modeling;
modeling of ecosystem development;
forecasting natural risks;
optimization of recovery measures;
automatic assessment of project effectiveness;
support for strategic management of natural resources.
System architecture
DREVO AI consists of several specialized modules.
Hydrology AI
Models:
precipitation;
surface runoff;
infiltration;
groundwater movement;
spring feeding;
operation of all nine cascades;
seasonal water balance;
flood processes.
Allows you to determine in advance:
places where new ravines are formed;
areas prone to flooding;
zones of insufficient infiltration;
system efficiencyMountain Sponge.
Climate AI
Analyzes:
air temperature;
humidity;
wind;
evaporation;
transpiration;
snow reserves;
probability of fog formation;
microclimatic changes.
Used in conjunction withDREVO Cloud & Mist Systemto control local humidity.
Ecology AI
Models:
forest growth;
development of plant communities;
distribution of species;
restoration of biodiversity;
mycorrhiza development;
accumulation of organic matter;
restoration of swamps;
succession of ecosystems.
Allows us to predict forest development for decades to come.
Soil AI
Analyzes:
humus formation;
soil structure;
humidity;
infiltration capacity;
carbon content;
biological activity.
The system predicts changes in soil fertility after the implementation of restoration measures.
Biodiversity AI
Evaluates:
diversity of species;
state of populations;
migration routes;
invasive species;
ecological connectivity;
ecosystem sustainability.
Risk AI
Automatically calculates risk:
floods;
drought;
forest fires;
landslides;
mudflows;
erosion;
forest degradation;
salinization of coastal areas.
Working with nine cascades
Cascade 1 - Ridges
The following are analyzed:
snow accumulation;
wind loads;
fog formation;
distribution of precipitation.
Cascade 2 - Upper Slopes
The following are simulated:
infiltration;
development of erosion;
efficiency of microterraces;
slope stability.
Cascade 3 - Middle slopes
Forecasts:
forest growth;
biomass accumulation;
humus development;
water-holding capacity of the forest "sponge".
Cascade 4 - Ravines and Streams
Calculated:
flow rate;
sediment transport;
threshold efficiency;
restoration of riverbeds.
Cascade 5 - Foothills
The following are determined:
infiltration volumes;
filling swimming pools;
replenishment of aquifers;
availability of water for agriculture.
Cascade 6 - Floodplains
Forecasts:
seasonal flooding;
restoration of swamps;
accumulation of alluvial soils;
dynamics of floodplain forests.
Cascade 7 - Estuary
The following are analyzed:
water quality;
sediment migration;
delta state;
risk of salinization;
replenishment of coastal aquifers.
Cascade 8 - Coastal Zone
The following are simulated:
coastal erosion;
sand transfer;
dune development;
stability of lagoons;
influence of storms.
Cascade 9 - Open Sea
The following are analyzed:
evaporation;
heat exchange;
state of marine ecosystems;
connection with atmospheric circulation;
impact on the regional climate.
Scenario modeling
DREVO AI allows you to assess the consequences of various scenarios in advance.
Extreme downpours
The system calculates:
water movement paths;
possible destruction;
efficiency of cascades;
the need for additional engineering solutions.
Drought
The following are determined:
rate of humidity decrease;
forest condition;
stability of springs;
need for additional hydration.
Forest fires
The system simulates:
probability of occurrence;
spread of fire;
influence of wind;
availability of water sources;
effectiveness of fire prevention measures.
Forest restoration
AI predicts:
growth rate;
biodiversity change;
carbon accumulation;
impact on water balance;
the time frame for the formation of a mature ecosystem.
Self-study
The system is constantly improving its models.
Data sources:
Living Mountain Observatory;
TREVO AeroSense Drone;
Mountain Digital Twin;
satellite observations;
field research;
climate data archive.
Each new season improves the accuracy of forecasts.
Decision support
DREVO AI generates recommendations for:
placement of new forest corridors;
construction of microterraces;
restoration of springs;
creation of infiltration basins;
erosion control;
optimization of fire safety infrastructure;
placement of new sensors.
Recommendations are a decision support tool and require expert review before implementation.
Integration
DREVO AI is the intelligent core of the entire platform.
It unites:
DREVO Mountain Sponge;
Mountain Springs Recovery;
Mountain Forest Corridors;
DREVO Cloud & Mist System;
Living Mountain Observatory;
Mountain Digital Twin;
TREVO AeroSense Drone;
DREVO Mountain Rover;
DREVO Smart Agriculture;
Atlantic & Mediterranean Coastal Water and Soil Resilience Initiative (AMCWSRI).
Expected results
After the implementation of the system the following is achieved:
increasing the effectiveness of recovery measures;
reducing the risk of engineering errors;
early detection of natural hazards;
increasing the sustainability of ecosystems;
reduction of monitoring and management costs;
acceleration of decision-making;
the possibility of long-term forecasting of watershed development;
accumulation of a scientific database for the management of natural areas.
Project mission
WOOD AIis the intellectual center of the projectDREVO Living Mountains. If Living Mountain Observatoryserves as the nervous system,TREVO AeroSense Drone- with eyes, andMountain Digital Twin— the digital brain of the territory, thenWOOD AIbecomes the analytical intelligence of the entire platform. It combines data on hydrological, climatic, and ecological processes, transforming them into forecasts and scientifically based recommendations that help restore mountain ecosystems faster, more accurately, and more resiliently in a changing climate.