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

Abstract

Droughts pose a severe environmental risk in countries that rely heavily on agriculture, resulting in heightened levels of concern regarding food security and livelihood enhancement. Bangladesh is highly susceptible to environmental hazards, with droughts further exacerbating the precarious situation for its 170 million inhabitants. Therefore, we are endeavouring to highlight the identification of the relative importance of climatic attributes and the estimation of the seasonal intensity and frequency of droughts in Bangladesh. With a period of forty years (1981–2020) of weather data, sophisticated machine learning (ML) methods were employed to classify 35 agroclimatic regions into dry or wet conditions using nine weather parameters, as determined by the Standardized Precipitation Evapotranspiration Index (SPEI). Out of 24 ML algorithms, the four best ML methods, ranger, bagEarth, support vector machine, and random forest (RF) have been identified for the prediction of multi-scale drought indices. The RF classifier and the Boruta algorithms shows that water balance, precipitation, maximum and minimum temperature have a higher influence on drought intensity and occurrence across Bangladesh. The trend of spatio-temporal analysis indicates, drought intensity has decreased over time, but return time has increased. There was significant variation in changing the spatial nature of drought intensity. Spatially, the drought intensity shifted from the northern to central and southern zones of Bangladesh, which had an adverse impact on crop production and the livelihood of rural and urban households. So, this precise study has important implications for the understanding of drought prediction and how to best mitigate its impacts. Additionally, the study emphasizes the need for better collaboration between relevant stakeholders, such as policymakers, researchers, communities, and local actors, to develop effective adaptation strategies and increase monitoring of weather conditions for the meticulous management of droughts in Bangladesh.

Comment on egusphere-2024-1069

“…Surprisingly, several more recently advocated topics of interest were not fully perceived as emerging topics by our data-driven analysis. Although we identified for example remote sensing and drought indices as emerging topics, machine learning (Al Mamun et al, 2024;Prodhan et al, 2022), compound events (Yin et al, 2023;Lesk et al, 2022) and early warning systems (Funk et al, 2019; FAO-WFP early warning analysis of acute food insecurity hotspots, 2020) were not amongst the emerging topics. Here, topics that should make a meaningful contribution to drought research must gain more momentum and more substance through publication numbers, research focus and funding mechanisms.…”

“…Despite the rise in machine learning approaches being applied for drought forecasting (Al Mamun et al, 2024;Prodhan et al, 2022), in this analysis machine learning and artificial intelligence were not identified as individual topics. In addition, early warning systems and compound events were not identified as distinctive topics although urgency for progress in these topics is perceived as high (FAO-WFP early warning analysis of acute food insecurity hotspots, 2020; Yin et al, 2023;Ridder et al, 2022).…”

Comment on egusphere-2024-1069

“…Surprisingly, several more recently advocated topics of interest were not fully perceived as emerging topics by our data-driven analysis. Although we identified for example remote sensing and drought indices as emerging topics, machine learning (Al Mamun et al, 2024;Prodhan et al, 2022), compound events (Yin et al, 2023;Lesk et al, 2022) and early warning systems (Funk et al, 2019; FAO-WFP early warning analysis of acute food insecurity hotspots, 2020) were not amongst the emerging topics. Here, topics that should make a meaningful contribution to drought research must gain more momentum and more substance through publication numbers, research focus and funding mechanisms.…”

“…Despite the rise in machine learning approaches being applied for drought forecasting (Al Mamun et al, 2024;Prodhan et al, 2022), in this analysis machine learning and artificial intelligence were not identified as individual topics. In addition, early warning systems and compound events were not identified as distinctive topics although urgency for progress in these topics is perceived as high (FAO-WFP early warning analysis of acute food insecurity hotspots, 2020; Yin et al, 2023;Ridder et al, 2022).…”

Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh

“…For the adaptation, BRRI-developed saline-tolerant varieties like BRRI dhan47 and BRRI dhan67 are gaining popularity in the saline regions, but to cover more area, we need to develop higher saline (>16 dsm -1 ) tolerant varieties at vegetative and reproductive stages. The northern regions of Bangladesh are prone to drought stress due to factors such as reduced rainfall, groundwater depletion, and inadequate water drainage [64]. Many cultivable lands are remained fallow due to lack of water and drought conditions.…”