Clustering Lakes in Kampar by Using Morphometry Data and Machine Learning Analysis

Lakes have an important role in hydrological and biochemical cycle. It also has some other crucial role such as domestic and industrial water use as well as irigation. The monitoring and management of this aquatic resources is crucial. But with many numbers of lakes, it is very challenging to manage them all. Clustering lakes can provide the answer so the management of the same cluster lakes may be done efficiently. Within this study, morphometry data of 6 lakes in Kampar Regency, Riau Province, were analyzed by using one of artificial intelligence branch which is machine learning. Morphometrical data are collected by using information geographic system. These data then categorized by using python language. This categorization based on data mining categorization algorithm named K-means. Based on the K-means machine learning clustering, the optimum cluster based on Elbow methid is k=3. But there is a possibility to look around for k=2. Based on K=3, cluster 3 is defined as the lowest values of all atribute. Based on k=2, the lowest value of morphometry data wiil be in the cluster 1.. These data will not only provide basic data such as total area, shape, width and length, but also help to understand the large scale hydrological models.

Economic and Sustainability Inequalities and Water Consumption of European Union Countries

Water scarcity is becoming a global concern for many reasons as its consumption increases. This research aimed to analyze sustainability inequalities in the water consumption of EU countries. Descriptive statistics using data for four AQUASTAT periods (2002, 2007, 2012, and 2017), and quotients for the AQUASTAT 2017 period, were calculated using a proposed econometric model. The main results were that countries with high GPD and population showed high water stress and total water withdrawal. Countries with lower industry-value-added-to-GDP quotients were among those with higher industrial water use efficiency, while low water-services-use-efficiency quotients were associated with high services value added to GDP. Suggestions for policymakers are provided and formula application guidelines for regional-level comparisons are described.

Silica Removal from a Paper Mill Effluent by Adsorption on Pseudoboehmite and γ-Al2O3

Effluent reuse is a common practice for sustainable industrial water use. Salt removal is usually carried out by a combination of membrane processes with a final reverse osmosis (RO). However, the presence of silica limits the RO efficiency due to its high scaling potential and the difficulty of cleaning the fouled membranes. Silica adsorption has many advantages compared to coagulation and precipitation at high pHs: pH adjustment is not necessary, the conductivity of treated waters is not increased, and there is no sludge generation. Therefore, this study investigates the feasibility of using pseudoboehmite and its calcination product (γ-Al2O3) for silica adsorption from a paper mill effluent. The effect of sorbent dosage, pH, and temperature, including both equilibrium and kinetics studies, were studied. γ-Al2O3 was clearly more efficient than pseudoboehmite, with optimal dosages around 2.5–5 g/L vs. 7.5–15 g/L. The optimum pH is around 8.5–10, which fits well with the initial pH of the effluent. The kinetics of silica adsorption is fast, especially at high dosages and temperatures: 80–90% of the removable silica is removed in 1 h. At these conditions, silica removal is around 75–85% (<50 mg/L SiO2 in the treated water).