Marine Ecosystem Monitoring and Ocean Health AI

Module: Sustainable Tech | Difficulty: Premium

Ocean Acidification

Coral Bleaching Threshold

Comparison

Python Implementation

import numpy as np
from sklearn.ensemble import IsolationForest

class OceanMonitor:
    def calculate_dhw(self, weekly_temps, sst_baseline):
        return sum((t - sst_baseline) / 7 for t in weekly_temps if t > sst_baseline)

    def predict_bleaching_risk(self, dhw):
        if dhw >= 8: return 'severe'
        elif dhw >= 4: return 'moderate'
        elif dhw >= 1: return 'possible'
        return 'low'

    def ocean_health_index(self, metrics):
        weights = {'pH': 0.2, 'DO': 0.25, 'temperature': 0.3, 'chlorophyll': 0.25}
        scores = {
            'pH': np.clip((metrics['pH'] - 7.5) / 0.8, 0, 1),
            'DO': np.clip(metrics['DO'] / 8, 0, 1),
            'temperature': 1 - np.clip(abs(metrics['temperature'] - 20) / 10, 0, 1),
            'chlorophyll': 1 - np.clip(metrics['chlorophyll'] / 20, 0, 1)
        }
        return sum(weights[k] * scores[k] for k in weights)

Research Insight: Deep learning models for coral reef health assessment achieve 89% accuracy in identifying bleaching stages.