AI Smart Waste Management and Collection System Development

Development of AI System for Smart Waste Collection and Management

Traditional waste collection works on schedule, independent of actual container fullness. Result: 40% of trips—visit half-empty containers, 20%—overflowing. Smart Waste optimizes routes by actual fullness.

Container Fullness Monitoring

IoT fill level sensors:

Ultrasonic (HC-SR04) or infrared sensors in container lid:

• Measure distance to waste surface
• Transmit via NB-IoT / LoRaWAN (energy-efficient protocols for battery power)
• Measurement interval: every 30–60 minutes

Computer vision from surveillance cameras:

Where sensors unavailable—estimate fullness from cameras:

• CNN (MobileNetV3) on container image → fullness level (0–20%, 20–50%, 50–80%, >80%)
• Training set: annotated photos of containers at different levels
• Classification accuracy: 88–93%

Fullness Forecasting

Fill level time series:

Each container—individual fill model:

• Patterns: residential buildings—morning and evening peaks, offices—evening peak
• Seasonality: holidays increase waste volume 20–35%
• Weather influence: rain—less activity, summer—more drink packaging

import pandas as pd
import numpy as np
from prophet import Prophet

class WasteContainerPredictor:
    """Waste container fullness forecast"""

    def fit(self, fill_level_history, container_id):
        """
        fill_level_history: TimeSeries of fullness level (0-100%)
        Forecast when container reaches 80% fullness
        """
        df = fill_level_history.reset_index()
        df.columns = ['ds', 'y']

        # Model sawtooth curve: grows until collection, then resets
        # For forecast: take only current incomplete fill cycle
        last_emptying = df[df['y'] < 10]['ds'].max()
        current_cycle = df[df['ds'] >= last_emptying].copy()

        model = Prophet(
            growth='linear',
            daily_seasonality=True,
            weekly_seasonality=True,
            changepoint_prior_scale=0.3
        )
        model.fit(current_cycle)

        # Forecast until 80% fullness reached
        future = model.make_future_dataframe(periods=48, freq='H')
        forecast = model.predict(future)

        full_time = forecast[forecast['yhat'] >= 80]['ds'].min()
        return full_time

    def predict_collection_priority(self, all_containers, current_time):
        """Rank containers by collection urgency"""
        priorities = []
        for cid, container in all_containers.items():
            current_fill = container['current_fill_pct']
            predicted_full_time = self.fit(container['history'], cid)
            hours_until_full = (predicted_full_time - current_time).total_seconds() / 3600

            priority_score = current_fill + (1 / max(hours_until_full, 0.5)) * 10
            priorities.append((cid, priority_score, current_fill, predicted_full_time))

        return sorted(priorities, key=lambda x: -x[1])

Waste Collection Route Optimization

Dynamic Routing:

Each day (or multiple times per day) optimal routes built:

• Container list for collection: level >75% or expected fullness within 24h
• VRP optimization: multiple waste trucks + depot + containers
• Account: truck capacity, driver work time, convenient routes

Economic effect:

• Reduce trips: 30–45% (collect only full)
• Reduce mileage: 20–30% (optimal routes)
• Reduce overflowing: >80% (preventive collection)

Waste Sorting

AI-sorter for secondary materials:

At waste sorting facilities—computer vision for classification:

• Conveyor belt + cameras + RGB + NIR spectroscopy
• Classification: PET, PEHD, glass, cardboard, metal, organics, other
• Pneumatic ejectors → direct to correct bin
• Sorting accuracy 90–95% vs. 70–75% manual

Hazardous waste detection:

Batteries, mercury thermometers, aerosols—should not reach sorting facilities:

• Spectroscopy (LIBS/XRF) + classifier → stop conveyor + operator alert
• Statistics: reduce equipment damage from batteries by 80%

Analytics and Reporting

GIS monitoring:

Dispatcher web portal: container map with color-coded fullness:

• Green (<50%), yellow (50–75%), red (>75%)
• Waste truck tracking in real-time
• Route history and KPIs

Environmental reporting:

• Waste volumes by category (regulatory forms)
• Recycling rate: % of collected waste directed to processing
• Forecast: additional landfill or sorting facility capacity needed

Development timeline: 3–5 months for Smart Waste system with IoT monitoring, fullness forecast and route optimization.