Chandrakant Singh

TL;DR: The amount of plastic waste recovered and the revenue generated by the active informal recyclers in the study area have been evaluated and three non-linear machine learning models i.e., artificial neural network (ANN), support vector machine (SVM) and random forest (RF) have been developed and compared for the prediction of plastic Waste Generation rate.

TL;DR: In this paper , the authors propose a green water planetary boundary based on root-zone soil moisture and demonstrate that widespread green water modifications now present increasing risks to Earth system resilience. But the green water boundary is not explicitly considered in the existing planetary boundaries framework that demarcates a global safe operating space for humanity.

TL;DR: In this article, the authors analyzed and quantified the occurrence of forest-savanna transitions along transects in South America and Africa and classified the ecosystem's adaptability to droughts into four classes of drought coping strategies.

TL;DR: In this article , the authors empirically study tropical forests' stability and hydroclimatic adaptation dynamics by examining remotely sensed tree cover change (ΔTC; aboveground ecosystem structural change) and root zone storage capacity (Sr; buffer capacity towards water-stress) over the last two decades.

TL;DR: In this article , the authors propose a green water boundary within the existing planetary boundaries framework, of which a control variable could be defined as the percentage of ice-free land area on which root-zone soil moisture deviates from Holocene variability for any month of the year.