ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool by D. Di Santo, C. He, F. Chen, L. Giovannini

“…Through a detailed case study with the Weather Research and Forecasting (WRF) model coupled with the Noah-MP land surface model, ML-AMPSIT is demonstrated to efficiently predict the effects of varying the values of Noah-MP model parameters with a relatively small number of model runs by simulating a sea breeze circulation over an idealised flat domain. …”
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Characteristics, distribution patterns and efforts to handle macroplastic waste on the border of the Musi river, Palembang city, Indonesia by Putri Mega Kusuma, Utaya Sugeng, Sumarmi, Bachri Syamsul

“…Data collection techniques use documentation and survey instruments as check-lists to record waste generation found on river borders. …”
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