Comprehensive overview of flood modeling approaches: A review of recent advances
As one of nature's most destructive calamities, floods cause fatalities, property destruction,
and infrastructure damage, affecting millions of people worldwide. Due to its ability to …
and infrastructure damage, affecting millions of people worldwide. Due to its ability to …
Deep learning in hydrology and water resources disciplines: Concepts, methods, applications, and research directions
KP Tripathy, AK Mishra - Journal of Hydrology, 2023 - Elsevier
Deep Learning (DL) methods have gained significant recognition in hydrology and water
resources applications in recent years. Beginning with a discussion on fundamental …
resources applications in recent years. Beginning with a discussion on fundamental …
Artificial neural network approaches for disaster management: A literature review
Disaster management (DM) is one of the leading fields that deal with the humanitarian
aspects of emergencies. The field has attracted researchers because of its ever-increasing …
aspects of emergencies. The field has attracted researchers because of its ever-increasing …
Evaluation of artificial intelligence models for flood and drought forecasting in arid and tropical regions
KE Adikari, S Shrestha, DT Ratnayake… - … Modelling & Software, 2021 - Elsevier
With the advancement of computer science, Artificial Intelligence (AI) is being incorporated
into many fields to increase prediction performance. Disaster management is one of the …
into many fields to increase prediction performance. Disaster management is one of the …
Short-term flood probability density forecasting using a conceptual hydrological model with machine learning techniques
Y Zhou, Z Cui, K Lin, S Sheng, H Chen, S Guo… - Journal of Hydrology, 2022 - Elsevier
Making accurate and reliable probability density forecasts of flood processes is
fundamentally challenging for machine learning techniques, especially when prediction …
fundamentally challenging for machine learning techniques, especially when prediction …
A new seq2seq architecture for hourly runoff prediction using historical rainfall and runoff as input
Abstract Machine learning especially deep learning methods have been widely used for
runoff prediction in recent years. Models based on the sequence-to-sequence (seq2seq) …
runoff prediction in recent years. Models based on the sequence-to-sequence (seq2seq) …
Real-time rainfall-runoff prediction using light gradient boosting machine coupled with singular spectrum analysis
Z Cui, X Qing, H Chai, S Yang, Y Zhu, F Wang - Journal of Hydrology, 2021 - Elsevier
Urban rainfall-runoff prediction is an effective method for flood mitigation. However, it is
difficult to realize real-time and accurate prediction due to the strong nonlinearity and …
difficult to realize real-time and accurate prediction due to the strong nonlinearity and …
[HTML][HTML] Predicting the performance of green stormwater infrastructure using multivariate long short-term memory (LSTM) neural network
The expected performance of Green Stormwater Infrastructure (GSI) is typically quantified
through numerical models based on hydrologic parameters and physics-based equations …
through numerical models based on hydrologic parameters and physics-based equations …
Stock index futures price prediction using feature selection and deep learning
WL Yan - The North American Journal of Economics and Finance, 2023 - Elsevier
Stock index futures allows stock investors to manage different kinds of risk. This paper
combines the AdaBoost feature selection and deep learning model for predicting stock index …
combines the AdaBoost feature selection and deep learning model for predicting stock index …
Flood Forecasting Using Hybrid LSTM and GRU Models with Lag Time Preprocessing
Climate change and urbanization have increased the frequency of floods worldwide,
resulting in substantial casualties and property loss. Accurate flood forecasting can offer …
resulting in substantial casualties and property loss. Accurate flood forecasting can offer …