Prediction of monthly dry days with machine learning algorithms: a case study in Northern Bangladesh

Abstract

Dry days at varied scale are an important topic in climate discussions. Prolonged dry days define a dry period. Dry days with a specific rainfall threshold may visualize a climate scenario of a locality. The variation of monthly dry days from station to station could be correlated with several climatic factors. This study suggests a novel approach for predicting monthly dry days (MDD) of six target stations using different machine learning (ML) algorithms in Bangladesh. Several rainfall thresholds were used to prepare the datasets of monthly dry days (MDD) and monthly wet days (MWD). A group of ML algorithms, like Bagged Trees (BT), Exponential Gaussian Process Regression (EGPR), Matern Gaussian Process Regression (MGPR), Linear Support Vector Machine (LSVM), Fine Trees (FT) and Linear Regression (LR) were evaluated on building a competitive prediction model of MDD. In validation of the study, EGPR-based models were able to better capture the monthly dry days (MDD) over Bangladesh compared to those by MGPR, LSVM, BT, LR and FT-based models. When MDD were the predictors for all six target stations, EGPR produced highest mean R2 of 0.91 (min. 0.89 and max. 0.92) with a least mean RMSE of 2.14 (min. 1.78 and max. 2.69) compared to other models. An explicit evaluation of the ML algorithms using one-year lead time approach demonstrated that BT and EGPR were the most result-oriented algorithms (R2 = 0.78 for both models). However, having a least RMSE, EGPR was chosen as the best model in one year lead time. The dataset of monthly dry–wet days was the best predictor in the lead-time approach. In addition, sensitivity analysis demonstrated sensitivity of each station on the prediction of MDD of target stations. Monte Carlo simulation was introduced to assess the robustness of the developed models. EGPR model declared its robustness up to certain limit of randomness on the testing data. The output of this study can be referred to the agricultural sector to mitigate the impacts of dry spells on agriculture.

Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm

“…The study measured the performance of each ML algorithm independently. ML algorithms employ various statistical, probabilistic, and optimization methods to extract useful patterns from large and complex datasets that are unstructured and derived from past experiences 61 . Time series data is characterized by a sequential order, where each observation is influenced by the preceding observations.…”

Flood Forecasting by Using Machine Learning: A Study Leveraging Historic Climatic Records of Bangladesh

“…However, several studies have been presented that forecast rainfall using various computer algorithms. Osmani et al's [15] innovative method for predicting monthly dry days (MDD) at six target stations in Bangladesh makes use of a variety of ML techniques. The datasets for monthly days without precipitation and monthly days with rainfall were produced using a range of rainfall limitations.…”

“…Our objective is to accurately predict new, unforeseen data. The most effective machine learning and deep learning methods for analysing the daily rainfall quantity forecasts have been chosen after evaluating many articles on rainfall prediction [8][9][10][11][12][13][14][15][16][17][18][19][20].…”

“…Similarly, effective machine learning has been utilised to construct a rain forecast model in numerous research [1,6,14]. Osmani et al [15] suggest a novel approach for predicting monthly dry days at six target points using several machine learning (ML) techniques. Various other studies, such as those of Manandhar et al [16], Gude et al [3], and Nguyen and Chen [4], have reported utilising the concepts of fuzzy logic, support vector and k-nearest neighbours approach.…”

Performance evaluation of Machine Learning based regression models for rainfall forecasting

“…Further research investigated the application of data mining techniques, such as CART, SVM, K-NN, RF, and LDA, to analyze their efficacy in predicting rainfall for Rajshahi, Bangladesh (M. M. Rahman et al, 2021). Expanding the scope of investigation, a study utilized various machine learning algorithms in conjunction with rainfall data to estimate the monthly dry days (Hossain et al, 2019(Hossain et al, , 2020Osmani et al, 2022;. For the purposes of this study, a specific rainfall threshold was utilized to classify a given day as either dry or wet.…”