With the rapid economic development, the water quality is getting worse in Haraz River Basin and the adjacent areas. To improve the water quality, the total inland pollutant load should be controlled effectively. Therefore, a simulation model for the prediction of the steady-state water quality in terms of BOD, based on Streeter Phelps equation, was developed. The Streeter-Phelps equation gives...

Hypoxia is a long-standing threat to the integrity of the Chesapeake Bay ecosystem. In this study, we introduce a Bayesian framework that aims to guide the parameter estimation of a Streeter–Phelps model when only hypoxic volume data are available. We present a modeling exercise that addresses a hypothetical scenario under which the only data available are hypoxic volume estimates. To address t...

We consider a mixed reaction diffusion system describing the organic pollution in stream-waters. It may be viewed as the static version of Streeter–Phelps equations relating the Biochemical Oxygen Demand and Dissolved Oxygen to which dispersion terms are added. In this work, we propose a mixed variational formulation and prove its well-posedness. Next, we develop two finite element discretizati...

The Mayang River in Jember Regency has a length of approximately 145.50 km which is the longest river and can irrigate an area 5,860 hectares. use by community produce waste, both domestic agricultural livestock waste. Waste that enters directly into bodies cause decrease water quality create pollution load. Therefore, measurement load capacity was conducted. samples were taken using grab sampl...

Rembagan River is the main tributary of Bedadung in Cangkring Village, Patrang District, Jember Region. Various community activities area such as bathing, washing, defecating and urinating well agricultural will produce domestic waste. These wast can cause river water pollution, especially quality degradation. then research needs to be done an analysis capacity receiving pollution costs. The pu...

It is proposed that a numerical environmental model cannot be justified for predictive tasks without an implicit uncertainty analysis which uses reliable and transparent methods. Various methods of uncertainty-based model calibration are reviewed and demonstrated. Monte Carlo simulation of data, Generalised Likelihood Uncertainty Estimation (GLUE), the Metropolis algorithm and a set-based appro...