Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines

By Riccardo Taormina, Kwok-Wing Chau. 2015

317 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/a9629cbe607b230b5de0412afa285327a2513492

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Biswajeet Pradhan
2. Xinqiu Zhao
3. Lin Jia
4. Xiaoyan Zhou
5. Feng Xu
6. Yanan Song
7. D. Tien Bui
8. Jiqiang Niu

In Environmental Engineering

1. D. Tien Bui
2. Biswajeet Pradhan
3. Po-Chun Chen
4. Xinqiu Zhao
5. Lin Jia
6. G. You
7. Chih-Chiang Wei
8. Yuan-Heng Wang

In Water Science and Technology

1. Po-Chun Chen
2. Xinqiu Zhao
3. Lin Jia
4. G. You
5. Chih-Chiang Wei
6. Yuan-Heng Wang

Citations in the corpus

• A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area (2017, methodology): “PSO is considered a powerful algorithm that has been widely used in soft computing for model optimization, such as in rainfall–runoff modeling (Taormina and Chau, 2015), flood susceptibility modeling (Tien Bui et al., 2016e), dam behavior modeling (Bui et al., 2016), and slope instability analysis…”
• Global Research on Artificial Intelligence from 1990-2014: Spatially-Explicit Bibliometric Analysis (2016, background): “AI has great advantage in reasoning, learning [47], data processing and application-specific problem-solving [48–51].”
• An Improved Particle Swarm Optimization (PSO) Optimized Integral Separation PID and its Application on Central Position Control System (2019, methodology): “The standard PSO has been proven to be a helpful and efficient method for PID tuning [10]–[12], especially in nonlinear systems [13], [14].”
• Comparison of methods for non-stationary hydrologic frequency analysis: Case study using annual maximum daily precipitation in Taiwan (2017, background): “BIC is with a logarithmic complexity one (Taormina and Chau, 2015).”
• Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation (2018, background): “The different watershed parameters also contain very complex relationships to construct these models [9].”