50 Project Ideas on AI and Sustainable Engineering

Definition of AI and Sustainable Engineering

Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, such as learning, reasoning, and self-correction. Sustainable engineering involves designing and implementing systems that meet current needs without compromising the ability of future generations to meet their own needs. AI can play a crucial role in advancing sustainable engineering practices by optimizing resource use, reducing waste, and improving overall efficiency in various industries.

Importance of integrating AI in sustainability efforts

By harnessing the power of AI, sustainable engineering can achieve innovative solutions that prioritize environmental conservation and long-term viability. The integration of AI in sustainability efforts can lead to significant advancements in technology and practices that benefit both current and future generations.

This integration can also help businesses and organizations make more informed decisions that have a positive impact on the environment. By utilizing AI algorithms to analyze data and identify patterns, companies can better understand their resource consumption and carbon footprint, leading to more sustainable practices. Additionally, AI can help predict potential environmental risks and provide solutions to mitigate them, ultimately creating a more resilient and sustainable future for all.

For example, a retail company could use AI algorithms to optimize their supply chain and reduce carbon emissions by streamlining transportation routes and minimizing excess inventory. By doing so, the company not only reduces its environmental impact but also saves money on fuel costs and storage expenses, leading to increased profitability and sustainability in the long run.

Another example could be a city using AI to analyze traffic patterns and optimize traffic flow, reducing congestion and cutting down on vehicle emissions. This not only improves air quality and reduces carbon footprint, but also enhances overall quality of life for residents by creating a more efficient and sustainable transportation system.

By incorporating AI into their operations, businesses can not only improve their bottom line but also contribute to the global effort to combat climate change. This proactive approach to sustainability not only benefits the environment but also enhances a company's reputation and appeal to environmentally-conscious consumers. Ultimately, the integration of AI into sustainable practices is a win-win for both businesses and the planet, paving the way for a more environmentally-friendly future for all.

These innovative ideas will showcase the potential for AI to revolutionize the way companies approach sustainability and drive positive change in the world.

The ideas shared here are divided into the following categories :

Content

CHAPTER I 11

INTRODUCTION

Introduction 13

Sustainable Engineering and Water Management 20

Artificial Intelligence in Water Resource Management 22

Integration and Synergies 24

Specific Applications and Examples 25

Research Gaps 26

CHAPTER II 33

ARTIFICIAL INTELLIGENCE

Different Types of Application of AI 35

Applications of AI: 38

Key Takeaways: 39

CHAPTER III 41

PROJECT IDEAS ON WATER RESOURCE MANAGEMENT

Investigate the long-term impacts of temperature overshoot on hydrology and water resources 43

Develop user-friendly applications for monitoring and analysing temporal changes in water bodies using satellite imagery, spectral indices like the Normalized Difference Water Index (NDWI), and cloud computing platforms such as Google Earth Engine (GEE) 43

Assess the dynamics of water resources in specific regions, such as the Mehla Block in India 44

Evaluate the effectiveness of different water conservation policies using psychological factors 44

Explore the integration of gender dimensions into water management research and policy 45

Develop and test decision support systems (DSS) for water management [15, 16] 45

Evaluate different approaches for public participation in water resource management. 45

Examine the role of social learning in water management 46

Analyze water use practices in key demand sectors, such as agriculture, domestic use, and industry, to identify areas where efficiency can be improved 46

Investigate alternative water supply sources for different demand sectors 47

CHAPTER IV 49

PROJECT IDEAS ON WATER POWER ENGINEERING

Optimizing micro-hydropower (MHP) turbine placement in water distribution networks 51

Developing integrated water-power models with cooling constraints 52

Exploring the use of pumps as turbines (PATs) for energy recovery in water systems 52

Designing predictive optimal water and energy irrigation (POWEIr) controllers 53

Investigating the potential of mini-hydro power generation on existing irrigation projects 54

Developing and implementing micro-hydro power (MHP) systems in agricultural canals 54

Exploring novel methods of energy harvesting from water flows 55

Assessing the impact of climate change on hydropower production 56

Developing and applying machine learning for improved water and energy management 56

Investigating the use of renewable energy sources to reduce reliance on thermal power 57

CHAPTER V 59

PROJECT IDEAS ON FLOOD MANAGEMENT

Developing new spectral indices for flood mapping 61

Integrating groundwater dynamics into flood risk management 61

Examining the role of spontaneous volunteers (SVs) in flood risk management 62

Improving flood forecasting and early warning systems 63

Transitioning from flood control to flood resilience 63

Analyzing the nonlinear spatial heterogeneity of urban flooding factors 64

Developing machine learning models for flood prediction in agricultural fields 65

Rethinking the relationship between flood risk perception and flood management 65

Using sketch maps and agent-based simulation for flood risk management 66

Examining the effectiveness of natural flood management (NFM) 67

CHAPTER VI 69

PROJECT IDEAS ON DROUGHT MANAGEMENT

Developing and applying multi-index drought assessments 71

Quantifying the nonlinear relationship between drought and ecological restoration project (ERP) effectiveness 71

Creating drought vulnerability maps 72

Integrating remote sensing for drought monitoring and feature extraction 73

Developing and implementing drought early warning systems 73

Assessing the effectiveness of water management strategies for drought mitigation 74

Investigating the use of unconventional water sources for drought resilience 74

Developing drought risk management frameworks 75

Optimizing irrigation methods for water conservation 75

Understanding drought impacts on community water resources and management 76

CHAPTER VII 77

PROJECT IDEAS ON SUSTAINABLE AGRICULTURE SOLUTIONS

Developing Predictive Optimal Water and Energy Irrigation 79

Optimising irrigation scheduling with solar power through Synchronized Pumping and Modulation (SPM) 79

Implementing Model Predictive Control (MPC) for precision irrigation 80

Assessing the impact of irrigation practices on water use efficiency 81

Investigating the potential of alternative water sources for irrigation 81

Developing smart irrigation monitoring and control strategies 82

Analysing the effectiveness of different irrigation methods on crop yields and water conservation 82

Applying machine learning for predicting floodwater levels in agricultural fields 83

Exploring the use of soil and water conservation techniques for rainfed agriculture 84

Evaluating the impact of climate change on irrigation water requirements 84

CHAPTER VIII 87

PROJECT IDEAS ON WATER QUALITY ENGINEERING

Developing augmented machine learning models for real-time sewage quality assessment 89

Investigating the use of ferrate for rapid biocidal control in sewer biofilms 90

Evaluating the effectiveness of nature-based solutions (NBS) for water quality improvement 90

Developing advanced treatment strategies for wastewater reuse 91

Assessing the impact of wastewater irrigation on groundwater quality 91

Creating integrated water quality indices (WQIs) for specific applications 92

Developing and testing methods for monitoring bacteriological contamination 92

Applying remote sensing and GIS for large-scale water quality monitoring 93

Investigating the impact of land use changes on groundwater quality 94

Exploring the use of machine learning for predicting groundwater contamination 94

CHAPTER IX 97

PROJECT IDEAS ON GROUNDWATER MANAGEMENT

Developing sustainable zoning management for ecological restoration in arid environments 99

Assessing the impact of artificial recharge on groundwater quality 99

Creating groundwater quality indices for specific uses 100

Analysing the spatial variability of groundwater quality using GIS 101

Investigating the relationship between groundwater salinization and natural/anthropogenic factors 101

Developing groundwater management strategies based on a system dynamics approach 102