The convergence of AI, IoT, and big data for advancing flood analytics research

Abstract

Floods are among the most destructive natural hazards that affect millions of people across the world leading to severe loss of life and damage to properties, critical infrastructure, and the environment. The combination of artificial intelligence (AI), big data, and the Internet of Things (IoTs), has the potential to more accurately predict these extreme events and accelerate the convergence of advanced techniques for flood analytics research. This convergence—so called the Artificial Intelligence of Things (AIoT)—is transformational for both technologies and science-based decision making since AI adds value to IoT through interpretable machine learning (ML) while IoT leverages the power of AI via connectivity and data intelligence. The aim of this research is to discuss the workflow of a Flood Analytics Information System (FAIS; version 4.00) as an example of AIoT prototype to advance and drive the next generation of flood informatics systems. FAIS integrates crowd intelligence, ML, and natural language processing (NLP) to provide flood warning with the aim of improving flood situational awareness and risk assessments. Various image processing algorithms, i.e., Convolutional Neural Networks (CNNs), were also integrated with the FAIS prototype for image label detection, and floodwater level and inundation areas calculation. The prototype successfully identifies a dynamic set of at-risk locations/communities using the USGS river gauge height readings and geotagged tweets intersected with watershed boundary. The list of prioritized locations can be updated, as the river monitoring system and condition change over time (typically every 15 min). The prototype also performs flood frequency analysis (FFA) by fitting multiple probability distributions to the annual flood peak rates and calculates the uncertainty associated with the model. FAIS was operationally tested (beta-tested) during multiple hurricane driven floods in the US and was recently released as a national-scale flood data analytics pipeline.

The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review

“…It involves deploying AI algorithms and techniques to analyze vast data collected from urban infrastructures and systems and diverse IoT devices embedded throughout the city's environment. This encompasses data from transportation systems, energy grids, water and waste management systems, smart buildings, public safety sensors, environmental monitoring devices, and other urban IoT deployments [ 8 , 10 , [13] , [14] , [15] , [16] , [74] ]. Urban AIoT aims to enhance cities' efficiency, sustainability, and livability by enabling intelligent decision-making, predictive analytics, and process automation across diverse domains.…”

“…[ 7 , 8 ] for detailed reviews). They rely heavily on data, AI algorithms, and AIoT tools to understand current environmental conditions, predict future trends, and make informed decisions to minimize environmental impact while promoting sustainability in smart cities and beyond [ [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [75] ]. In the context of sustainable smart cities, AIoT can enhance environmental sustainability strategies by enabling real-time monitoring, analysis, and optimization of resource use.…”

“…Targeted investigations pertain to renewable energies systems [ 10 ], hydrogen-based hybrid renewable energy systems [ 113 ], photovoltaic power generation forecasting [ 114 ], energy management systems [ 115 ], energy demand side [ 116 ], waste management [ 117 , 118 ], sustainable transportation development [ 119 ], ecosystem services [ 98 ], biodiversity protection [ 120 ], urban water resource management [ 99 , 121 ], air pollution [ 122 ], flood resistance [ 123 ], flood risk assessment [ 124 ], and flood prediction [ 16 ]. Important to note is that the energy sector is a primary user of AI in smart cities [ 8 , 125 ], with various technologies supporting the monitoring, analysis, and application of planning processes to combat pollution in urban environment.…”

“…This integrated approach highlights the synergistic potential of AI and AIoT technologies in reshaping urban planning strategies to address complex environmental challenges. Indeed, these technologies have significantly impacted multiple urban domains by optimizing resource management, enhancing energy efficiency, maximizing the utilization of renewable energy sources, streamlining waste management processes, improving transportation systems, preserving biodiversity, reducing environmental footprints, and mitigating the potential risks of climate change [ [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] ]. Through the provision of comprehensive environmental data, the facilitation of seamless integration of urban systems, and the coordination of various urban domains, AI and AIoT technologies closely align with the fundamental responsibilities of urban planners.…”

Comparative Analysis of Communication Technologies for Cloud-Based Flood Risk Predictive Modeling in Urban Areas

“…In their pioneering study, Khatibi et al [2] examine the synergies between Artificial Intelligence (AI), IoT, and Big Data in the context of flood analytics. Introducing the notion of AIoT (AI + IoT), the researchers offer the Flood Analytics Information System (FAIS), a sophisticated prototype meant to increase flood situational awareness and risk assessments.…”