As we enter 2026, global water resource management stands at a profound crossroads. The increasing frequency of extreme weather events driven by climate change, rapid socio-economic development, and rising ecological protection requirements are collectively posing unprecedented challenges to traditional water management models. Against this backdrop, leveraging next-generation information technologies to build a smart water network—enabling refined management, optimized allocation, and supply–demand balance of water resources—has shifted from a forward-looking concept to an urgent practical necessity.
The Intelligent Analysis Platform for Water Network Resource Allocation and Supply–Demand Balance (hereinafter referred to as “the Platform”) is a core product of this transformative wave. It is a comprehensive intelligent system integrating data acquisition, processing, analysis, modeling, forecasting, and decision support. The Platform aims to eliminate the “data silos” common in traditional water management by integrating multi-source heterogeneous data and applying advanced analytical models and algorithms to achieve dynamic perception, precise simulation, and scientific decision-making across the entire water resource value chain and all related elements.
01 Overall Architecture and Core Technologies of the Platform
The Platform is not based on a single technology; rather, it is a complex system engineering project integrating advanced technologies such as the Internet of Things (IoT), big data, cloud computing, artificial intelligence (AI), and digital twin technology. Its technical architecture typically adopts a layered design to ensure flexibility, scalability, and efficiency.
1.1 Technical Architecture: A Multi-Layer Collaborative "Smart Brain"
Based on various smart water conservancy and smart water utility development schemes, the typical technical architecture of the Platform can be divided into four main layers.
1.2 Core Technology Engines
1.2.1 Big Data and Cloud Computing
Big data technology addresses the challenges of storing, managing, and computing massive volumes of water-related information, while cloud computing provides elastic and scalable computing power for complex operations. By building a cloud-based big data center, the Platform can efficiently process real-time monitoring data streams and long-term historical datasets, support complex model computations and multi-user concurrent access, and significantly reduce system construction and maintenance costs.
1.2.2 Artificial Intelligence and Machine Learning
AI is the key to achieving "intelligence" within the Platform, and its applications span all aspects of water resource management:
Forecasting
Machine learning models (such as LSTM and ARIMA) are used to conduct short-, medium-, and long-term forecasts for urban water supply and agricultural irrigation demand, with significantly higher accuracy than traditional quota-based methods.
Identification and Diagnosis
Computer vision technologies analyze surveillance videos and remote sensing imagery to automatically identify river debris, crop growth conditions, and leakage points in water supply networks.
Optimization
Intelligent optimization algorithms such as genetic algorithms and particle swarm optimization are applied to solve complex problems—such as joint reservoir operation and optimal water allocation—under multiple constraints.
1.2.3 Digital Twin Technology
Digital twin technology represents a major breakthrough in the smart water sector in recent years. It tightly integrates physical water network systems with digital virtual models, enabling full-element digital mapping and intelligent simulation of the real world. Within the Platform, the digital twin engine plays a crucial role:
Panoramic Predictive Visualization
Builds 3D visualization scenarios from river basins to pipeline networks, from macro to micro levels, dynamically displaying real-time operational status.
Simulation and Scenario Deduction
Simulates the impacts of different dispatch schemes, extreme weather events, or policy changes within a virtual environment, enabling "what-you-see-is-what-you-get" scenario analysis.
Virtual–Real Integration and Intelligent Control
Feeds optimized control strategies derived from the virtual model back to physical infrastructure, forming a closed loop of "perception–analysis–decision–control".
02 Dynamic Updating Mechanism for Regional Water Use Accounts
Regional water use accounts form the foundation of refined water resource management. They record water consumption across four major sectors: agriculture, industry, domestic use, and ecology. Traditionally, updates were slow, relied on manual reporting, and suffered from poor timeliness and accuracy. By introducing new technologies, the Platform fundamentally transforms this process, enabling dynamic and precise updates.
2.1 Importance of the "Four-Water" Accounts
Dynamically updated accounts for agricultural, industrial, domestic, and ecological water use provide the core basis for implementing total water consumption control and quota management. They underpin water rights allocation, water fee collection, water-saving eva1uation, supply–demand balance analysis, carrying capacity assessment, and deficit forecasting. Accurate and real-time accounts allow managers to clearly understand where water is used, how much is consumed, and how efficiently it is utilized—providing a solid foundation for scientific decision-making.
2.2 Key Technologies and Methods
2.2.1 Smart Metering and IoT Monitoring
This is the most direct method for capturing water use data at the source:
Domestic and Industrial Water Use
Large-scale deployment of smart water meters (AMI) with remote transmission capabilities enables minute-level or hourly automatic data collection.
Agricultural Irrigation
Installation of ultrasonic flow meters and radar water level gauges at intake points and key nodes within irrigation districts.
Ecological Water Use
Monitoring water levels and flows at key cross-sections, combined with ecological water demand models, enables dynamic accounting of environmental water consumption.
2.2.2 Remote Sensing (RS) and Geographic Information Systems (GIS)
For vast agricultural areas, ground monitoring alone is insufficient. Remote sensing provides an efficient large-scale solution:
Irrigated Area Verification
High-resolution satellite imagery combined with AI image recognition accurately identifies actual crop planting areas.
Crop Evapotranspiration Estimation
Using remote sensing and meteorological data, evapotranspiration models (such as SEBAL and METRIC) estimate actual farmland water consumption.
GIS Integrated Analysis
Visualizes water users, intake points, and monitoring stations on GIS maps and overlays them with administrative boundaries, water function zones, and land use types for spatial analysis.
2.2.3 Data Fusion and Multi-Source Validation
The Platform integrates multi-source data for cross-verification and validation to enhance accuracy. For example, an industrial enterprise's water consumption can be compared across smart meter readings, permitted withdrawal volumes, production output data (combined with sectoral quotas), and wastewater discharge monitoring data. Significant discrepancies automatically trigger alerts for further inspection.
2.3 Dynamic Update Process
Based on the above technologies, the dynamic update process of water use accounts follows an automated workflow integrating data acquisition, processing, validation, and real-time database updates.
03 Water Resources Carrying Capacity Assessment and Multi-Scenario Supply–Demand Gap Forecasting
With accurate regional water use data in place, the Platform shifts its focus to future assessment and forecasting: Is the region's water sufficient? How large might future deficits be? This requires complex carrying capacity assessment models and multi-scenario supply–demand forecasting models.
3.1 Water Resources Carrying Capacity Assessment Model
Water resources carrying capacity refers to the maximum scale of socio-economic development that a regional water system can sustainably support within a given period and technological–economic level. It is a core scientific basis for regional development planning and ecological red-line delineation.
3.1.1 Model Framework
As a complex systemic concept involving water resources, society, economy, and ecology, carrying capacity assessment models are generally categorized into three types:
Comprehensive Indicator eva1uation Method
Constructs multi-indicator eva1uation systems and calculates composite scores or grades.
System Dynamics (SD) Model
Analyzes dynamic feedback relationships among system components, suitable for long-term simulation and policy analysis.
Supply–Demand Balance Analysis Method
eva1uates carrying capacity based on the balance between available water supply and total demand.
3.1.2 Commonly Used Models
The Platform typically integrates multiple models for different precision and scale requirements:
Combined Weight–TOPSIS Model
Ranks eva1uation units by measuring relative closeness to optimal and worst solutions, reducing subjectivity in single-weighting methods.
System Dynamics (SD) Simulation Model
Simulates long-term evolution trends under different development pathways.
Water Footprint Theory
Measures total water consumption—including blue water, green water, and gray water—embedded in products and services.
3.1.3 Key Indicator: Water Use Efficiency (WUE)
Water Use Efficiency (WUE) is a core indicator reflecting water management and technological levels. It directly determines the socio-economic benefits achievable under limited water supply.
Definition and Measurement
At the macro level, indicators such as water consumption per ¥10,000 GDP or per ¥10,000 industrial added value reflect economic efficiency, while the effective utilization coefficient of irrigation water reflects agricultural efficiency.
Efficiency eva1uation Model
Data Envelopment Analysis (DEA) is widely used to assess regional comprehensive WUE by constructing production frontiers and eva1uating relative efficiency under multiple inputs and outputs.
3.2 Multi-Scenario Supply–Demand Gap Forecasting
Future water supply and demand are influenced by uncertainties such as climate change, economic growth patterns, industrial restructuring, and water-saving policy implementation. Single-trend extrapolation is no longer sufficient. The Platform must therefore support multi-scenario forecasting.
3.2.1 Scenario Design
Typical scenarios include:
Baseline Scenario
Assumes continuation of current socio-economic trends and climate conditions.
High Economic Growth Scenario
Assumes higher-than-expected GDP and population growth, increasing water demand.
Extreme Drought Scenario
Simulates severe drought events (e.g., once-in-10-year or once-in-50-year), sharply reducing surface water availability.
Strict Water-Saving Policy Scenario
Assumes full implementation of high-efficiency water-saving technologies and strict quota management.
Industrial Structure Optimization Scenario
Assumes transition toward low-water-consumption, high-value-added service and high-tech industries.
3.2.2 Forecasting Models and Tools
Under defined scenarios, the Platform invokes corresponding models to forecast future water supply and demand volumes.
3.2.3 Supply–Demand Gap Analysis and Accuracy eva1uation
By comparing projected demand and supply under each scenario, the Platform calculates potential supply–demand gaps. It also eva1uates forecast accuracy to ensure reliability. Through multi-scenario analysis, decision-makers gain a comprehensive understanding of future water conditions and can formulate appropriate response strategies.
