scholarly journals The Classification Status of River Water Quality in Riau Province Using Modified K-Nearest Neighbor Algorithm with STORET Modeling and Water Pollution Index

2021 ◽  
Vol 1783 (1) ◽  
pp. 012020
Author(s):  
Mustakim ◽  
Rosdina ◽  
Dian Ramadhani ◽  
M. Afdal ◽  
Medyantiwi Rahmawita
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
River Water ◽  
Nearest Neighbor ◽  
Pollution Index ◽  
River Water Quality ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Water Pollution Index ◽  
K Nearest Neighbor Algorithm

scholarly journals Assessment of Cau River water quality assessment using a combination of water quality and pollution indices

2020 ◽  
Vol 69 (2) ◽  
pp. 160-172 ◽  
Author(s):  
Cao Truong Son ◽  
Nguyen Thị Huong Giang ◽  
Trieu Phuong Thao ◽  
Nguyen Hai Nui ◽  
Nguyen Thanh Lam ◽  
...  
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
River Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Water Status ◽  
Pollution Index ◽  
River Water Quality ◽  
Quality Analysis ◽  
Pollution Indices

Abstract This research aims at using a combined water quality index (WQI) and pollution index (PI) to assess and characterize river water quality of Cau River which is one of the longest rivers in the north of Vietnam. Five different water quality and water pollution indices were used including the Water Quality Index (WQI), Comprehensive Pollution Index (CPI), Organic Pollution Index (OPI), Eutrophication Index (EI), and Trace Metal Pollution Index (TPI). The combined water pollution indices show more serious pollution towards the river downstream. In particular, CPI and OPI reveal a high risk of eutrophication. Cluster analysis was applied to classify water monitoring points into different quality groups in order to provide a better understanding of the water status in the river. This study indicates that a combined water quality analysis could be an option for decision making water use purposes while its single index shows the current situation of water quality.

scholarly journals STATUS MUTU AIR SUNGAI SIRAU SUB DAS SIRAU DI DAS BARITO KABUPATEN BARITO TIMUR PROVINSI KALIMANTAN TENGAH

2019 ◽  
Vol 15 (2) ◽  
pp. 271
Author(s):  
Ringkas Lelunuto ◽  
Muhammad Ruslan ◽  
Kissinger Kissinger ◽  
Fatmawati Fatmawati
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
River Water ◽  
Pollution Control ◽  
Government Regulation ◽  
Pollution Index ◽  
Class Ii ◽  
River Water Quality ◽  
Water Pollution Control

 Sirau River is one of the rivers in the Barito Timur Regency which flows through the Dusun Timur District. Sirau River water is used as raw material for regional water companies (PDAM) and is used by communities around the river to meet the needs of other daily life such as MCK and other uses and is also used as a landfill, domestic wastewater and waste water mining and plantation activities both directly and indirectly. This study aims to analyze the status of pollution of the Sirau river through the approach of the pollution index value, assess the quality of the Sirau River water and identify the source of pollutants entering the Sirau River. The results showed that in general the water quality of the Sirau River for pH, COD, BOD, Fe, and Mn parameters exceeds the water quality standard namely Government Regulation No. 82 of 2001 class II concerning Water Quality Management and Water Pollution Control, the main source of pollutants comes from residential domestic waste, coal mining activities and dry leaves that fall into river streams. Sirau river water quality from upstream to downstream based on river water quality analysis using the pollution index method shows that water quality has decreased where the river conditions are generally mildly polluted. The quality of lightly polluted Sirau river water cannot be utilized in accordance with the designation of class II water based on Government Regulation No. 82 of 2001 concerning Management of Water Quality and Water Pollution Control.

scholarly journals Analysis of water pollution index around gold mining in the downstream of Krueng Kluet sub-watershed

2022 ◽  
Vol 951 (1) ◽  
pp. 012050
Author(s):  
H Basri ◽  
Manfarizah ◽  
H C Prayudi
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
River Water ◽  
Government Regulation ◽  
Gold Mining ◽  
Pollution Index ◽  
Quality Standards ◽  
Mining Area ◽  
Water Quality Standards ◽  
Water Pollution Index

Abstract Water is one of the primary needs of every living creature. With the increase in population, the need for water continues to increase. The declining water quality caused by human activities is one of the world’s concerns. This study examines river water quality status in the gold mining area in the downstream of Krueng Kluet sub-watershed. The method used to determine the level of river water pollution is based on the Decree of Minister of Environment Number 115 the Year 2003, which uses class 1 water quality standards according to Government Regulation of The Republic of Indonesia Number 82 the Year. The results show that river water in the study area is in the category that is not polluted or fulfills water quality standards for drinking water and daily needs.

Handwritten Balinesse Character Recognition using K-Nearest Neighbor

2018 ◽  
Author(s):  
I Wayan Agus Surya Darma
Keyword(s):  
Feature Extraction ◽  
Success Rate ◽  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Extraction Process ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Character Feature

Balinese character recognition is a technique to recognize feature or pattern of Balinese character. Feature of Balinese character is generated through feature extraction process. This research using handwritten Balinese character. Feature extraction is a process to obtain the feature of character. In this research, feature extraction process generated semantic and direction feature of handwritten Balinese character. Recognition is using K-Nearest Neighbor algorithm to recognize 81 handwritten Balinese character. The feature of Balinese character images tester are compared with reference features. Result of the recognition system with K=3 and reference=10 is achieved a success rate of 97,53%.

scholarly journals Detection Model on Fatigue Driving Behaviors Based on the Operating Parameters of Freight Vehicles

2021 ◽  
Vol 11 (15) ◽  
pp. 7132
Author(s):  
Jianfeng Xi ◽  
Shiqing Wang ◽  
Tongqiang Ding ◽  
Jian Tian ◽  
Hui Shao ◽  
...  
Keyword(s):  
Traffic Accidents ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Operating Data ◽  
Detection Model ◽  
Driving Behaviors ◽  
K Nearest Neighbor Algorithm ◽  
Fatigue Driving ◽  
Freight Vehicles

Whether in developing or developed countries, traffic accidents caused by freight vehicles are responsible for more than 10% of deaths of all traffic accidents. Fatigue driving is one of the main causes of freight vehicle accidents. Existing fatigue driving studies mostly use vehicle operating data from experiments or simulation data, exposing certain drawbacks in the validity and reliability of the models used. This study collected a large quantity of real driving data to extract sample data under different fatigue degrees. The parameters of vehicle operating data were selected based on significant driver fatigue degrees. The k-nearest neighbor algorithm was used to establish the detection model of fatigue driving behaviors, taking into account influence of the number of training samples and other parameters in the accuracy of fatigue driving behavior detection. With the collected operating data of 50 freight vehicles in the past month, the fatigue driving behavior detection models based on the k-nearest neighbor algorithm and the commonly used BP neural network proposed in this paper were tested, respectively. The analysis results showed that the accuracy of both models are 75.9%, but the fatigue driving detection model based on the k-nearest neighbor algorithm is more reliable.

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