Gradient-Based Optimisers Versus Genetic Algorithms in Deep Learning Architectures: A Case Study on Rainfall Estimation Over Complex Terrain

Gradient-Based Optimisers Versus Genetic Algorithms in Deep Learning Architectures: A Case Study on Rainfall Estimation Over Complex Terrain Yash Bhisikar1*, Nirmal Govindaraj1*, Venkatavihan Devaki2*, Ritu Anilkumar3 1Birla Institute of Technology And Science, Pilani, K K Birla Goa Campus 2Birla Institute of Technology And Science, Pilani, Pilani Campus 3North Eastern Space Applications Centre, Department of Space, Umiam E-mail: f20210483@goa.bits-pilani.ac.in * Authors have contributed equally to this study. Rainfall is a crucial factor that affects planning processes at various scales, ranging from agricultural activities at the village or residence level to governmental initiatives in the domains of water resource management, disaster preparedness, and infrastructural planning. Thus, a reliable estimate of rainfall and a systematic assessment of variations in rainfall patterns is the need of the hour. Recently, several studies have attempted to predict rainfall over various locations using deep learning architectures, including but not limited to artificial neural networks, convolutional neural networks, recurrent neural networks, or a combination of these. However, a major challenge in the estimation of rainfall is the chaotic nature of rainfall, especially the interplay of spatio-temporal components over orographically complex terrain. For complex computer vision challenges, studies have suggested that population search-driven optimisation techniques such as genetic algorithms may be used in the optimisation as an alternative to traditional gradient-based techniques such as Adam, Adadelta and SGD. Through this study, we aim to extend this hypothesis to the case of rainfall estimation. We integrate the use of population search-based techniques, namely genetic algorithms, to optimise a convolutional neural network architecture built using PyTorch. We have chosen the study area of North-East India for this study as it receives significant monsoon rainfall and is impacted by the undulating terrain that adds complexity to the rainfall estimation. We have used 30 years of rainfall data from the ERA5 Land daily reanalysis dataset with a spatial resolution of 11,132 m for the months of June, July, August and September. Additionally, datasets of the following meteorological variables that can impact rainfall were utilised as input features: dew point temperature, skin temperature, net incoming short-wave radiation received at the surface, wind components and surface pressure. All the datasets are aggregated to daily time steps. Several configurations of the U-Net architecture, such as the number of hidden layers, initialisation techniques and optimisation algorithms, have been used to identify the best configuration in the estimation of rainfall for North-East India. Genetic algorithms were used in initialisation and optimisation to assess the ability of population search heuristics using the PyGAD library. The developed rainfall prediction models were validated at different time steps (0-day, 1-day, 2-day and 3-day latency) on a 7:1:2 train, validation, test dataset split for evaluation metrics such as root mean square error (RMSE) and coefficient of determination (R-squared). The evaluation was performed on a pixel-by-pixel basis as well as an image-by-image basis in order to take magnitude and spatial correlations into consideration. Our study emphasises the importance of considering alternate optimising functions and hyperparameter tuning approaches for complex earth observation challenges such as rainfall prediction.