Vogeti, RK and Jauhari, R and Mishra, BR and Srinivasa Raju, K and Nagesh Kumar, D (2024) Deep learning algorithms and their fuzzy extensions for streamflow prediction in climate change framework. In: Journal of Water and Climate Change, 15 (2). pp. 832-848.
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Abstract
The present study analyzes the capability of convolutional neural network (CNN), long short-term memory (LSTM), CNN-LSTM, fuzzy CNN, fuzzy LSTM, and fuzzy CNN-LSTM to mimic streamflow for Lower Godavari Basin, India. Kling-Gupta efficiency (KGE) was used to evaluate these algorithms. Fuzzy-based deep learning algorithms have shown significant improvement over classical ones, among which fuzzy CNN-LSTM is the best. Thus, it is further considered for streamflow projections in a climate change context for four-time horizons using four shared socioeconomic pathways (SSPs). Average streamflow in 2041-2060, 2061-2080, and 2081-2090 are compared to that of 2021-2040 and it changed by +3.59, +7.90, and +12.36 for SSP126; +3.62, +8.28, and +12.96 for SSP245; +0.65, -0.01, and -0.02 for SSP370; +0.02, +0.71, and +0.06 for SSP585. In addition, two non-parametric tests, namely, Mann-Kendall and Pettitt were conducted to ascertain the trend and change point of the projected streamflow. Results indicate that fuzzy CNN-LSTM provides a more precise prediction than others. The identified variations in streamflow across different SSPs facilitate valuable insights for policymakers and relevant stakeholders. It also paves the way for adaptive decision-making. © 2024 The Authors.
| Item Type: | Journal Article |
|---|---|
| Publication: | Journal of Water and Climate Change |
| Publisher: | IWA Publishing |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | Climate change; Convolutional neural networks; Decision making; Learning algorithms; Stream flow, Climate; Convolutional neural network; Fuzzy; Fuzzy extension; Mann-Kendall; Nonparametric tests; Socio-economics; Streamflow; Streamflow prediction; Time horizons, Long short-term memory |
| Department/Centre: | Division of Mechanical Sciences > Civil Engineering |
| Date Deposited: | 08 Apr 2024 09:06 |
| Last Modified: | 08 Apr 2024 09:06 |
| URI: | https://eprints.iisc.ac.in/id/eprint/84696 |
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