Intensively monitoring river water level and flows in both upstream and downstream catchments are essential for flood forecasting in disaster risk reduction. This paper presents a developed flood river water level forecasting utilizing a hybrid technique called adaptive neuro-fuzzy inference system (ANFIS) model, employed for Kelantan river basin, Kelantan state, Malaysia. The ANFIS model is designed to forecast river water levels at the downstream area in hourly lead times. River water level, rainfall, and river flows were considered as input variables located in upstream stations, and one river water level in the downstream station is chosen as flood forecasting point (FFP) target. Particularly, each of these input-output configurations consists of four stations located in different areas. About twenty-seven data with fifteen minutes basis recorded in January 2013 to March 2015 were used in training and testing the ANFIS network. Data preprocessing is done with feature reduction by principal component analysis and normalization as well. With more attributes in input configurations, the ANFIS model shows better result in term of coefficient correlation ( ) against artificial neural network (ANN)-based models and support vector machine (SVM) model. In general, it is proven that the presented ANFIS model is a capable machine learning approach for accurate forecasting of river water levels to predict floods for disaster risk reduction and early warning.
One of the problems involving water bodies is the pollution of water sources, which later becomes the parent of various social, economic, and health problems. One of these conditions can be overcome by utilizing phytoremediation through constructed wetlands. HYDRUS is a software that offers easy modelling of pollutant decay mechanisms in water bodies and constructed wetlands using finite element method. This study aims to show and analyse how HYDRUS software can model the mechanism in constructed wetland by using finite element method. The parameters observed are the time for the pollutant, namely ammonia, nitrate, and inorganic phosphorous, to reach the outlet, and the response curve of the pollutant loading on the model. From the simulation, it can be inferred that the maximum velocity of the water going through the constructed wetland is 8.11 m/day, or 811 cm/day. The dominating velocity in the wetland is around 160 cm/day, or 1.6 m/day. The response curve of the pollutant transport is also in accordance with theoretical response for impulse loading. The result yields the effectivity of simulated constructed wetland, which are 90.33% for ammonia and nitrate, and 90.26% for inorganic phosphorous. The result yields quite optimistic effectivity of the constructed wetland, which may be caused by the assumptions made in the model, which goes through simplification method. It can also be caused by the differences in plants assumption. In the HYDRUS simulation, the plant used is grass, which does not specify what kind of grass. Meanwhile, the physical simulation uses water bamboo.
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