Enterprise AI Analysis
Urban water demand forecasting via artificial neural network models: a case study in Southern Brazil
Brazil's growing water consumption and the problems arising from its shortage in urban centers are concerning issues. This study performs an exploratory analysis on water demand and applies demand forecast models using artificial neural networks to a city in southern Brazil. For that, twenty-four short-term demand forecast models were proposed for each assessed demand category (residential, commercial, industrial, public and total). The chosen artificial neural network was the Multilayer Perceptron (MLP) with structures of one and two hidden layers, trained using the Backpropagation (BP) and Resilient Backpropagation (RP) methods. The results showed that the RP-trained two-hidden-layer neural networks are more accurate for forecasts in the residential, industrial, public and total categories, while BP-trained networks perform better in the commercial category. The models including both past demands and the other independent variables showed the best results. The most accurate model was obtained in the total water demand category with a 1.59% mean absolute percentage error (MAPE), followed by residential (1.91% MAPE), commercial (1.94% MAPE), industrial (3.02% MAPE) and public (4.57% MAPE) categories.
Executive Impact Summary
Our analysis of 'Urban water demand forecasting via artificial neural network models' reveals critical insights for enterprise leaders in water management, urban planning, and infrastructure development. The study demonstrates the robust capability of Artificial Neural Networks (ANNs), specifically Multilayer Perceptron (MLP) models with Resilient Backpropagation (RP) training, to forecast urban water demand with high accuracy in Southern Brazil. This predictive power is vital for optimizing resource allocation, improving operational efficiency, and informing strategic infrastructure investments.
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Lowest MAPE (Total Demand)
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Short-Term Models Proposed
RP
Superior Training Method
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Optimal Hidden Layers
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Total Demand
Residential
Commercial
Industrial
Public
This tab provides detailed analysis for the Total water demand category, highlighting key findings related to AI-driven water demand forecasting. The models achieved the lowest MAPE in this category (1.59%), showcasing high predictive accuracy for overall urban water planning.
This tab provides detailed analysis for the Residential water demand category, highlighting key findings related to AI-driven water demand forecasting. This category represents the largest consumption share (80.06%) and showed a MAPE of 1.91%, making it a critical focus for demand management.
This tab provides detailed analysis for the Commercial water demand category, highlighting key findings related to AI-driven water demand forecasting. For this category, BP-trained networks performed better, achieving a MAPE of 1.94%, indicating specific model suitability for commercial patterns.
This tab provides detailed analysis for the Industrial water demand category, highlighting key findings related to AI-driven water demand forecasting. This category showed a MAPE of 3.02%, reflecting the unique and potentially more variable consumption patterns of industrial users.
This tab provides detailed analysis for the Public water demand category, highlighting key findings related to AI-driven water demand forecasting. This category showed a MAPE of 4.57%, suggesting that public consumption might be influenced by factors requiring further specialized modeling approaches.
Methodology Flowchart
The research methodology adopted a structured approach, starting from data collection and systematization, moving through modeling with different neural network architectures and training methods, leading to forecasting, and finally assessing accuracy.
Data Collection and Systematization
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Modeling
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Single-Hidden-Layer Neural Network
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Double-Hidden-Layer Neural Network
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Backpropagation
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Resilient Backpropagation
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Forecast
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Accuracy
| Method | Key Advantages | Observed MAPE (Test Phase) |
|---|---|---|
| Artificial Neural Networks (This Study) |
|
1.59% (Total Demand), 1.91% (Residential) |
| SARIMA (Similar Study) |
|
1.19% (Total Demand), 2.08% (Commercial), 15.74% (Public) |
| ETS (Similar Study) |
|
1.89% (Residential) |
Comparing ANN performance with other common forecasting methods (like SARIMA and ETS) used in similar studies reveals superior accuracy, especially over longer forecast horizons.
40.26°C
Highest Recorded Temperature (Jan 2018)
Maximum temperature emerged as a highly significant variable in residential and commercial water demand models, underscoring the direct impact of climate on consumption patterns. This peak coincides with periods of increased residential and commercial water usage, validating its importance in predictive models.
Joinville: A Blueprint for Urban Water Management
The city of Joinville's comprehensive water supply system, serving a large urban population in Southern Brazil, provides an excellent real-world application for advanced demand forecasting. Understanding its diverse consumption categories (residential, commercial, industrial, public) allows for granular and highly targeted AI model development.
Key Facts about Joinville's Water System:
- Tenth largest city in Santa Catarina, Brazil.
- Population: 604,708 (2021 estimate).
- Water supply system serves 65% (Cubatão WTS) and 35% (Piraí WTS) of demand.
- 13 reservoirs with 51,676 m³ total capacity.
- 2252 km water supply network.
- Residential category accounts for 80.06% of total water consumption.
Quantify Your AI Advantage
Use our interactive calculator to estimate the potential cost savings and efficiency gains your enterprise could realize by implementing AI-driven predictive analytics for water demand management.
Estimated Annual Savings
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Annual Hours Reclaimed
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AI Implementation Roadmap for Predictive Water Management
Our phased approach ensures a smooth and effective integration of AI into your water demand forecasting operations, maximizing value and minimizing disruption.
Phase 1: Data Integration & Model Training
Integrate historical consumption data, climate variables, and consumer unit data. Train MLP models using RP and BP with varying hidden layer structures and lag configurations.
Phase 2: Validation & Performance Tuning
Validate models against historical test sets using MAPE, R², MAE, RMSE, MSE, and NRMSE. Fine-tune hyperparameters for optimal accuracy and identify best-performing architectures.
Phase 3: Real-time Deployment & Monitoring
Deploy selected models into a real-time forecasting system. Continuously monitor performance and retrain models with new data to maintain accuracy and adapt to changing conditions.
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