Deep learning algorithms and their fuzzy extensions for streamflow prediction in climate change framework

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. HIGHLIGHTS Fuzzy CNN-LSTM shows a significant improvement in KGE in training and testing periods over others.; Incorporating a fuzzy inference layer in deep learning algorithms has substantially improved peak flow simulation.; Mann–Kendall and Pettitt tests were conducted to ascertain the trend and change point of the projected streamflow of the basin.;

Keywords

• climate
• cnn
• fuzzy
• lstm
• streamflow