Bangladesh significantly depends on seasonal and annual rainfall for
agriculture. Yet, there are flash or regional floods and droughts affecting human-lives,
properties, and crops that urge low-cost but accurate warning systems in meeting
sustainable developments. The paper aims to predict rainfall in Bangladesh by
incorporating the Artificial Neural Network with optimizations based on climatology.
The study-site is focused on metropolitans: Dhaka (capital) and Chittagong (port) cities
and compared with historical evolutions. The experiments include data mining and a
statistical approach for trend analyses before modeling. The observation data, i.e., 24-
hr accumulated rainfall (mm), is obtained from Bangladesh Meteorological Department
(1989 to 2014) and Ogimet (1999 to 2018). September is a neutral and transitional
month from monsoon to winter to evaluate drought scenarios. Additionally, in Matlab
R2018b, Nonlinear Autoregressive with external input (NARX) is tested with several
optimization techniques: Levenberg-Marquardt (LM), Bayesian Regularization (BR),
and Scaled-Conjugate Gradient. ANN models show that Chittagong has more rainfall
than Dhaka supporting climatological statistics. Specifically, forecasts for Dhaka are
25%, 21%, and 22%, and Chittagong 31%, 30%, and 8%, respectively, using LM, BR,
and SCG. The iterations for Chittagong 12, 201, and 10 and Dhaka are 5, 12, and 47,
respectively, by LM, BR, and SCG. The results suggest rainfall probabilities in
September about 20 to 30% of annual events. The study, particularly for Chittagong in
ANN, refers to computational resources and time that are significant to test sensitivities
before building a meteorological disaster management tool.
Keywords: Agriculture, Artificial intelligence, Bangladesh, Bayesian
Regularization, Computation, Conjugate gradient, Climatology, Development,
Economy, Levenberg-Marquardt, Meteorological Droughts, Metropolitans,
NARX, Neural Network, Optimization, Prediction, Rainfall, Sustainability.
