PDF

Conceptual Framework for Developing Predictive Models for Environmental Risk Assessment in Agricultural Ecosystems

Sadat Itohan Ihwughwavwe

2025enenvironmental riskpredictive modelingagricultural ecosystemsmachine learningremote sensingsustainable agriculture

Abstract

Language:

Environmental risk assessment in agricultural ecosystems is essential for safeguarding sustainable food production, ecosystem health, and climate resilience. The paper notes that conventional ERA methods struggle to capture the complex and dynamic interactions among biophysical, climatic, and human factors. It proposes a comprehensive conceptual framework for predictive modeling that integrates machine learning, data-driven analytics, and environmental process-based simulations to quantify risks such as soil degradation, nutrient leaching, water contamination, and biodiversity loss. The framework emphasizes multi-scale data fusion from remote sensing, IoT-based field sensors, and geospatial databases to enable spatiotemporal forecasting and scenario analysis. It also highlights the need for uncertainty quantification, explainable AI, and stakeholder-inclusive validation to improve model transparency and policy relevance. By linking predictive analytics with environmental monitoring systems, the study promotes proactive risk mitigation, sustainable land management, and evidence-based agricultural policy.

Download

Cite This Work

@article{f2e13465-e039-4b5c-88cd-3477b7b21a0f,
  title={Conceptual Framework for Developing Predictive Models for  Environmental Risk Assessment in Agricultural Ecosystems  },
  author={Sadat Itohan Ihwughwavwe},
  year={2025},
  language={en}
}
TY  - JOUR
TI  - Conceptual Framework for Developing Predictive Models for  Environmental Risk Assessment in Agricultural Ecosystems  
AU  - Sadat Itohan Ihwughwavwe
PY  - 2025
LA  - en
ER  -

Similar Items

Biotechnology in Agriculture - A Review

Sunil Jayant Kulkarni

Majority of the population in developing countries earns their livelihood by agriculture. Food scarcity is one of the major problems faced by develope

2017enPDF

Artificial intelligence-aided materials design: AI-algorithms and case studies on alloys and metallurgical processes

Rajesh Jha, Bimal Kumar Jha

This book describes the application of artificial intelligence (AI) and machine learning (ML) concepts to develop predictive models that can be used t

2022enPDF

Effect of Roasting Temperature for Copper Leaching of Sulfide Concentrate by Combined Methods

Introduction Concerns over air pollution and the environmental problem of acid rain have made governments all over the world tighten their regulations

2023enPDF

Equation Driven Design and Validation of a Reverberatory Furnace for Non ferrous Metals

Roger Rumbu check

2025enPDF$30.00

Extraction of copper and the co-leaching behaviour of other metals from waste printed circuit boards using alkaline glycine solutions

Huan Li, Elsayed Oraby, Jacques Eksteen

Waste printed circuit boards (WPCBs) are a complicated and valuable fraction of electric and electronic waste. The recycling of them is critical to av

2025enPDF

Recovery of valuable metals from mining and mineral processing waste

Roger Rumbu

2025enPDF