Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 2021

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Predicting Tachypleus gigas Spawning Distribution with Climate Change in Northeast Coast of India

Pati Siddhartha, Shahimi Salwa, Nandi Debabrata, Sarkar Tanmay, Acharya Satya N., Sheikh Hassan I., Acharya Dipti Kanta, Choudhury Tanupriya, Akbar John B., Nelson Bryan R., Dash Bisnu Prasad, Edinur Hisham Atan

Journal of Ecological Engineering

2021

Vol. 22, nr 3

211--219

Species distribution models are used to predict ideal grounds, species range, and spatial shifts in an ecology over a span of time. With an aim to use Maximum entropy model (MaxEnt), presence records and pseudo-absence points are used to predict the Tachypleus gigas spawning activity for 2030 and 2050 in northeast India. The bearings of sixty T. gigas spawning grounds identified in 2018 were inserted into ArcGIS v.10.1. Meanwhile, 19 environment variables were inserted into MaxEnt v. 3.3.3, before the model performance was tested using receiver operational characteristics and area under curve (AUC). With an AUC of 0.978,85% was achieved for isothermality (bio3) and 74% for temperature (x̄= average) of the wettest quarter (bio8), all of which were inserted into ArcGIS to produce spatial maps. Although we learnt that T. gigas are still spawning in Odisha in 2030 and 2050, their distribution range is predicted to shrink due to the coastal morphology change. The climate conditions in Odisha revolve with the monsoon, summer and winter seasons from which, temperature variations do not only influence the annual absence/presence of spawning adults but also, the survival of juveniles in natal beaches. The use of MaxEnt offers novelty to predict population sustainability of arthropods characterized by oviparous spawning (horseshoe crabs, turtles, terrapins and crocodiles) through which, the government of India can take advantage of the present data to initiate the coastal rehabilitation measures to preserve their spawning grounds.

Hydro-Geochemical Attributes Based Classifiers for Groundwater Analysis

Mishra Partha Sarathi, Nandi Debabrata, Sahu Pramod Chandra, Mohanta Kamal Lochan, Edinur Hisham Atan, Sarkar Tanmay, Pati Siddhartha

Ecological Engineering & Environmental Technology

2021

Vol. 22, iss. 5

28--39

Freshwater supply is critical for domestic, agriculture and industrial purposes. A good supply of clean water is normally obtained from surface and groundwater water bodies. Nonetheless, many localities rely heavily on the later as the main source of their water resource. Therefore, proper mapping, exploitation and conservation of groundwater resources should become a primary focus in years to come. In this study, groundwater samples collected from Bamanghati, Odisha were assigned into three classes (excellent, good and bad) based on guidelines provided by World Health Organization in 1984 These water quality assignments were completed via a combined approach of hydro-geochemical information and artificial neural network for reconstructing a classifier for groundwater analysis. Here, the probabilistic approach and boosted instance selection method were used to remove inconsistencies in the dataset and to determine the classification accuracy, respectively. Finally, the transmuted dataset is used for kernel estimator-based Bayesian and Decision tree (J48) classification approaches. Findings from the present study confirm that the preprocessing task using statistical analysis along with the combined method of hydro-geochemical attributes-based classification approach is encouraging while the decision tree approach is better than the Bayesian neural network classifier in terms of precision, recall, F-measures, and Kappa statistics.