Deep learning algorithms and their fuzzy extensions for streamflow prediction in climate change framework
Rishith Kumar Vogeti, Rahul Jauhari, Bhavesh Rahul Mishra, K. Srinivasa Raju, G. V. Nagesh Kumar
Abstract
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.