Developing a novel tool for assessing the groundwater incorporating water quality index and machine learning approach

Unraveling agricultural water pollution despite an ecological policy in the Ayeyarwady Basin

“…This financial insufficiency manifests in several vital areas: diminished capacity for enforcement and monitoring of environmental regulations, constrained initiation and sustainability of conservation projects, limited scope in conducting public awareness and educational campaigns and delayed or aborted development of essential ecological infrastructure. Echoing the appeal by extant studies for targeted technical and financial interventions [ 5 , 52 – 54 ], restricted funding adversely affects research and development initiatives vital for informed policymaking and curtails training and capacity building necessary for effective policy implementation. It hampers collaborative efforts with various stakeholders in Ayeyarwady.…”

Application of machine learning techniques to predict groundwater quality in the Nabogo Basin, Northern Ghana

“…Notably, severally conventional methods have been applied to assess water resources, groundwater vulnerability and quality around the globe [ [11] , [12] , [13] , [14] , [15] , [16] , [17] ]. In recent years, Water Quality Index (WQI) integrated with Machine Learning (ML) and Deep Learning, as many innovative techniques [ 18 ]. Deshpande et al [ 19 ], Goodarzi et al [ 20 ], Patel et al [ 21 ], Siriwardhana et al [ 22 ],Abba et al [ 23 ] and Xiong et al [ 24 ] assessed water, and groundwater quality in their various study areas, where their findings from WQI methods proved significant for water quality management, and it recommended the replication of these methods in different territories of the world.…”

Measuring the impact of responsible factors on CO2 emission using generalized additive model (GAM)

“…Several researchers used different techniques to predict their target variables. Nowadays, several nonlinear models, machine learning, and/or artificial algorithms are widely used in several fields for prediction purposes though the interpretation of parameters is quite challenging [ [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] ]. However, the potential influence of responsible variables for reducing emission in Bangladesh has become an interesting issue among cutting-edge researchers, there are few studies utilizing non-parametric approaches to investigate the potential.…”