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An accurate prediction of the spatial distribution of phytoplankton biomass, as represented by Chlorophyll-a (CHL-a) concentrations, is important for assessing ecological conditions in the marine environment. This study developed a hyperparameter-optimized decision tree-based machine learning (ML) models to predict the geographical distribution of marine phytoplankton CHL-a in the Bay of Bengal. To predict CHL-a over a large spatial extent, satellite-derived remotely sensed data of ocean color features (CHL-a, colored dissolved organic matter, photosynthetically active radiation, particulate organic carbon) and climatic factors (nighttime sea surface temperature, surface absorbed longwave radiation, sea level pressure) from 2003 to 2022 are used to train and test the models. Results obtained from this study have shown the highest concentrations of CHL-a occurred near the Bay's coastal belts and river estuaries. Analysis revealed that aside from photosynthetically active radiation, organic components exhibited a stronger positive relationship with CHL-a than climatic features, which are correlated negatively. Results showed the chosen decision tree methods to all possess higher R 2 and lower root mean square error (RMSE) errors. Furthermore, XGBoost outperforms all other models in predicting the geographic distribution of CHL-a. To assess the model efficacy on seasonal basis, a best performing XGBoost model was validated in the Bay of Bengal region which has shown a good performance in predicting the spatial distribution of Chl-a as well as the pixel values during the summer, winter and monsoon seasons. This study provides the best ML model to researchers for predicting CHL-a in the Bay of Bengal. Further it helps to improve our knowledge of CHL-a spatial dynamics and assist in monitoring marine resources in the Bay of Bengal. It worth noting that the water quality in the Indian Ocean is very dynamic in nature, therefore, additional efforts are needed to test the efficacy of this study model over different seasons and spatial gradients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Assessment of eco-toxicant using bioluminescent bacterial assay is a widely used and globally accepted method. In this work, a new luminescent bacterium was isolated from squid (Loligo duvauceli) and identified as Photobacterium leiognathi strain AK-MIE using 16S rRNA, phylogeny analysis. The predicted optimum conditions by RSM were 2.76% (w/v) NaCl, 2.28% (w/v) peptone, 0.34% (w/v) yeast extract, and pH 6.83 with 541,211.80 RLU of luminescent production whereas the predicted optimum conditions by ANN were 2.21% (w/v) NaCl, 2.27% (w/v) peptone, 0.39% (w/v) yeast extract, and pH 6.94 which produced 541,986.20 RLU. The validation analysis of both RSM and ANN show 0.60% and 0.69% deviation from the predicted results indicating that both models provided good quality predictions with ANN showing a superior data fitting capability for non-linear regression analysis. Toxicity tests show strain AK-MIE was sensitive to mercury (concentration causing 50% inhibition or IC 50 of 0.00978 mgL -1 ), followed by cadmium (IC 50 of 0.5288 mgL -1 ), copper IC 50 of (0.8117 mgL -1 ), silver (IC 50 of 1.109 mgL -1 ), and lead (IC 50 of 10.71 mgL -1 ) which are more sensitive than previously isolated luminescent bacteria, suggesting that strain AK-MIE has the potential to be used in toxicity assessment of heavy metals in the environment. Based on the field trial results, several sediment samples from industrial areas in Bangi, Selangor managed to inhibit the bioluminescence of strain AK-MIE. Validation method carried out using ICP-MS proved the presence of several toxic heavy metal elements., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Inc. All rights reserved.)