Journal article
Evolution of Data-Driven Flood Forecasting: Trends, Technologies, and Gaps—A Systematic Mapping Study
Water, Vol.17(15), pp.1-33
31/07/2025
Metrics
Abstract
This paper presents a Systematic Mapping Study (SMS) on data-driven approaches in flood forecasting from 2019 to 2024, a period marked by transformative developments in Deep Learning (DL) technologies. Analysing 363 selected studies, this paper provides an overview of the technological evolution in this field, methodological approaches, evaluation practices and geographical distribution of studies. The study revealed that meteorological and hydrological factors constitute approximately 76% of input variables, with rainfall/precipitation and water level measurements forming the core predictive basis. Long Short-Term Memory (LSTM) networks emerged as the dominant algorithm (21% of implementations), whilst hybrid and ensemble approaches showed the most dramatic growth (from 2% in 2019 to 10% in 2024). The study also revealed a threefold increase in publications during this period, with significant geographical concentration in East and Southeast Asia (56% of studies), particularly China (36%). Several research gaps were identified, including limited exploration of graph-based approaches for modelling spatial relationships, underutilisation of transfer learning for data-scarce regions, and insufficient uncertainty quantification. This SMS provides researchers and practitioners with actionable insights into current trends, methodological practices, and future directions in data-driven flood forecasting, thereby advancing this critical field for disaster management.
Details
- Title
- Evolution of Data-Driven Flood Forecasting: Trends, Technologies, and Gaps—A Systematic Mapping Study
- Creators
- Banujan Kuhaneswaran - Southern Cross UniversityGolam Sorwar - Southern Cross UniversityAli Reza Alaei - Southern Cross UniversityFeifei Tong - Southern Cross University
- Publication Details
- Water, Vol.17(15), pp.1-33
- Publisher
- MDPI AG
- Grant note
- This research was funded by the Connectivity Innovation Network (CIN) (52251), an initiative of the New South Wales (NSW) Government and the NSW Telco Authority, Australia.
- Identifiers
- 991013303326202368
- Copyright
- © 2025 by the authors.
- Academic Unit
- Information Technology; Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article