Multiple regression modelling approach for rainfall prediction using large-scale climate indices as potential predictors

Rasel, H. M.; Imteaz, Monzur Alam; Mekanik, Fatemeh

doi:10.1504/ijw.2017.085879

Cited by 13 publications

(10 citation statements)

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“…Similar findings were presented by Rasel et al. (2016), Khastagir et al. (2022), Mekanik and Imteaz (2012), and Wei et al.…”

Section: Conclusion and Discussionsupporting

confidence: 88%

“…The findings indicated that these factors reasonably estimated the observed rainfall better than individual factors, implying that considering the synergistic effects of these influencing factors is important in interpreting and predicting the SON rainfall variations over SESA. Similar findings were presented by Rasel et al (2016), Khastagir et al (2022), Imteaz (2012), andWei et al (2023), but they focused on South, West, Victoria Australian rainfall and Arabian Peninsula rainfall, respectively. The authors demonstrated that rainfall predictability significantly enhanced based on the joint rainfall predictors relative to their separate Journal of Geophysical Research: Atmospheres 10.1029/2023JD040309 considerations.…”

Section: Conclusion and Discussionsupporting

confidence: 81%

“…It has been revealed that the SAM can cause considerable climatic impact in other areas. In particular, it is a crucial precursor for rainfall in several regions, including the east and southwest regions of Australia (Lim et al, 2013(Lim et al, , 2021Lim & Hendon, 2015;Rasel et al, 2016) and West Africa (Sun et al, 2010). Most of these studies have considered isolated effects of the SAM, which limits the predictability accuracy (Rasel et al, 2016).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

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The Role of the Subtropical and Extratropical Southern Hemisphere Anomalous Sea Surface Temperature in the Trans‐Seasonal Influence of Southern Annular Mode on Rainfall Over Southeastern South America

Mbigi,

Xiao

2024

JGR Atmospheres

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The current study explores the linkage and possible mechanisms of the Southern Annular Mode (SAM) with rainfall variability over southeastern South America (SESA). The findings indicate that the preceding May SAM can modulate the subsequent rainfall over the SESA region in September‐October‐November (SON), independent of the El Niño‐Southern Oscillation (ENSO) signal. Further analysis suggests that the SAM‐rainfall relationship is mainly due to the response of sea surface temperature (SST) in the subtropical and extratropical Southern Hemisphere to SAM variability via air‐sea interactions. Such SST anomalies persist to the subsequent SON and stimulate an upstream wave train extending into the study region from the South Paciﬁc. Such wave patterns form a cyclone anomaly over SESA, indicating strong subsidence and reduced convection over the region. Meanwhile, the low‐level anomalous cyclone and anticyclone located over the subtropical South Atlantic (on the coast of the study region) are associated with easterly winds to the South (North) of the anomalous cyclone (anticyclone), which advect colder and drier air into the study region due to the cold SST over the subtropical South Atlantic and leading to increased moisture scarcity and decreased rainfall over SESA. Besides, model results based on the joint climate indices (i.e., SAM, ENSO and Indian Ocean Dipole (IOD)) revealed a satisfactory estimate of the SON rainfall compared with either single index, indicating that the SAM can be one of the precursors of the SESA rainfall, besides ENSO and IOD.

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“…Similar findings were presented by Rasel et al. (2016), Khastagir et al. (2022), Mekanik and Imteaz (2012), and Wei et al.…”

Section: Conclusion and Discussionsupporting

confidence: 88%

Section: Conclusion and Discussionsupporting

confidence: 81%

Section: Conclusion and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

The Role of the Subtropical and Extratropical Southern Hemisphere Anomalous Sea Surface Temperature in the Trans‐Seasonal Influence of Southern Annular Mode on Rainfall Over Southeastern South America

Mbigi,

Xiao

2024

JGR Atmospheres

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“…Harnessing the power of artificial intelligence, researchers have increasingly turned to machine learning (ML) as a potent tool for predicting groundwater storage (GWS). Unlike traditional methods Rasel, 2018;Rasel et al, 2017;Rasel, Esha, et al, 2016;, ML algorithms excel at extracting hidden patterns and trends from vast datasets, continuously refining their predictive accuracy over time Rasel, Imteaz, Hossain, et al, 2015). This remarkable self-learning capability, coupled with minimal human intervention, makes ML a formidable asset for modeling and forecasting GWS dynamics.…”

Section: Relevant Literaturementioning

confidence: 99%

Machine Learning for Groundwater Storage Prediction: Leveraging Climatic Variables

Md.,

2024

Preprint

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Once abundant and readily available, groundwater (GW) is now dwindling at an alarming rate. This vital resource is under growing pressure from both natural and human-induced factors. Groundwater Level (GWL) is closely related to Groundwater Storage (GWS) thus the decline in GWL creates a shortage in GWS. This research developed a robust predictive model for GWS in Rajshahi district, Bangladesh, for the period 2001–2022 using six climatic variables, namely, Mean Temperature, Cloud Coverage, Humidity (percent), Solar Radiation, Sunshine, and Wind Speed. Three Machine Learning (ML)-based regression models- Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM) were applied for this purpose. Results showed that the accuracy level was quite high while RF regression was plugged into the observed dataset (R2 = 0.80). Moreover, among the six climatic variables, cloud coverage, humidity, and wind speed contributed 87.4% altogether to predict the GWS. These findings offer valuable insights not only for understanding the GWS dynamics in Rajshahi district but also for informing sustainable management strategies. By providing decision-makers with a clear understanding of the key climatic drivers and their impact, this research empowers them to implement effective interventions and conservation measures to ensure the long-term availability of this critical resource.

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“…To fill this gap, this present study spatiotemporally examined rainfall trends on a monthly and seasonal scale. The majority of previous research on rainfall predicting has used Linear Regression 64 , Adaptive Neuro-Fuzzy Inference System (ANFIS) 65 , Genetic Algorithm (GA) 66 , Mann Kendall test, Deep Learning Approach, Feed Forward Neural Network (FFNN), Empirical and Dynamical Methods, and Autoregressive Integrated Moving Average (ARIMA) 10 , 11 , 46 , 67 – 70 . Some innovative strategies have been widely used in recent years: wavelet transforms, couple-wavelet neural networks 71 , genetic algorithms, and uncertainty analysis for rainfall prediction 9 .…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal analysis and predicting rainfall trends in a tropical monsoon-dominated country using MAKESENS and machine learning techniques

Monir,

Rokonuzzaman,

Sarker

et al. 2023

Sci Rep

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Spatiotemporal rainfall trend analysis as an indicator of climatic change provides critical information for improved water resource planning. However, the spatiotemporal changing behavior of rainfall is much less understood in a tropical monsoon-dominated country like Bangladesh. To this end, this research aims to analyze spatiotemporal variations in rainfall for the period 1980–2020 over Bangladesh at seasonal and monthly scales using MAKESENS, the Pettitt test, and innovative trend analysis. Multilayer Perception (MLP) neural network was used to predict the next 8 years' rainfall changes nationally in Bangladesh. To investigate the spatial pattern of rainfall trends, the inverse distance weighting model was adopted within the ArcGIS environment. Results show that mean annual rainfall is 2432.6 mm, of which 57.6% was recorded from July to August. The Mann–Kendall trend test reveals that 77% of stations are declining, and 23% have a rising trend in the monthly rainfall. More than 80% of stations face a declining trend from November to March and August. There is a declining trend for seasonal rainfall at 82% of stations during the pre-monsoon, 75% during the monsoon, and 100% during the post-monsoon. A significant decline trend was identified in the north-center during the pre-monsoon, the northern part during the monsoon, and the southern and northwestern portions during the post-monsoon season. Predicted rainfall by MLP till 2030 suggests that there will be little rain from November to February, and the maximum fluctuating rainfall will occur in 2025 and 2027–2029. The ECMWF ERA5 reanalysis data findings suggested that changing rainfall patterns in Bangladesh may have been driven by rising or reducing convective precipitation rates, low cloud cover, and inadequate vertically integrated moisture divergence. Given the shortage of water resources and the anticipated rise in water demand, the study's findings have some implications for managing water resources in Bangladesh.

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Multiple regression modelling approach for rainfall prediction using large-scale climate indices as potential predictors

Cited by 13 publications

References 0 publications

The Role of the Subtropical and Extratropical Southern Hemisphere Anomalous Sea Surface Temperature in the Trans‐Seasonal Influence of Southern Annular Mode on Rainfall Over Southeastern South America

The Role of the Subtropical and Extratropical Southern Hemisphere Anomalous Sea Surface Temperature in the Trans‐Seasonal Influence of Southern Annular Mode on Rainfall Over Southeastern South America

Machine Learning for Groundwater Storage Prediction: Leveraging Climatic Variables

Spatiotemporal analysis and predicting rainfall trends in a tropical monsoon-dominated country using MAKESENS and machine learning techniques

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