scholarly journals Predicting BOD under Various Hydrological Conditions in the Dongjin River Basin Using Physics-Based and Data-Driven Models

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1383
Author(s):  
Eunjeong Lee ◽  
Taegeun Kim
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Fuzzy Inference ◽  
Oxygen Demand ◽  
Data Driven ◽  
Anfis Model ◽  
Hydrological Simulation ◽  
Inference System ◽  
Hydrological Conditions ◽  
Irrigation Period

The water quality of the Dongjin River deteriorates during the irrigation period because the supply of river maintenance water to the main river is cut off by the mass intake of agricultural weirs located in the midstream regions. A physics-based model and a data-driven model were used to predict the water quality in the Dongjin River under various hydrological conditions. The Hydrological Simulation Program–Fortran (HSPF), which is a physics-based model, was constructed to simulate the biological oxygen demand (BOD) in the Dongjin River Basin. A Gamma Test was used to derive the optimal combinations of the observed variables, including external water inflow, water intake, rainfall, and flow rate, for irrigation and non-irrigation periods. A data-driven adaptive neuro-fuzzy inference system (ANFIS) model was then built using these results. The ANFIS model built in this study was capable of predicting the BOD from the observed hydrological data in the irrigation and non-irrigation periods, without running the physics-based model. The predicted results have high confidence levels when compared with the observed data. Thus, the proposed method can be used for the reliable and rapid prediction of water quality using only monitoring data as input.

A Fuzzy Inference System for Water Quality of Chunnambar River, Puducherry

2015 ◽  
Vol 787 ◽  
pp. 322-326 ◽  
Author(s):  
V. Nirmala ◽  
K.R. Leelavathy ◽  
Sivapragasam Sowndharya ◽  
Parthiban Bama
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Fuzzy Inference System ◽  
Quality Index ◽  
Fuzzy Inference ◽  
Oxygen Demand ◽  
Pollution Level ◽  
Inference System ◽  
Indian Water ◽  
Proposed Model

A Fuzzy Inference System (FIS) is considered as an effective tool for solution of many complex engineering systems when ambiguity and uncertainty is associated with the systems. The water quality is an important issue of relevance in the context of present times. The proposed model is designed to predict Water Quality Index (WQI) for Chunnambar, Ariyankuppam, Puducherry Region, Southern India. A systematic investigation of the pollution level at Chunnambar from March 2013 to February 2014 was carried out. The untreated domestic wastes from various parts of the Ariyankuppam town are directly discharged into the river which leads to increased level of pollution. The present studies emphasis on the magnitude of pollution by monitoring key water quality parameters such as Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), pH and Temperature. FIS simplifies and speed up the computation of WQI as compared to the currently existing standards. In this paper, the proposed model is compared with Indian Water Quality Index (IWQI) and it is found that the designed model predicts accurately.

scholarly journals Modelling and Prediction of Water Quality by Using Artificial Intelligence

2021 ◽  
Vol 13 (8) ◽  
pp. 4259
Author(s):  
Mosleh Hmoud Al-Adhaileh ◽  
Fawaz Waselallah Alsaade
Keyword(s):  
Artificial Intelligence ◽  
Water Quality ◽  
Fuzzy Inference ◽  
Feed Forward Neural Network ◽  
Statistical Parameters ◽  
Anfis Model ◽  
K Nearest Neighbors ◽  
Inference System ◽  
Efficient Operation ◽  
Sanitation Systems

Artificial intelligence methods can remarkably reduce costs for water supply and sanitation systems and help ensure compliance with the quality of drinking and wastewater treatment. Therefore, modelling and predicting water quality to control water pollution has been widely researched. The novelty of the proposed system is presented to develop an efficient operation of monitoring drinking water to ensure a sustainable and friendly green environment. In this work, the adaptive neuro-fuzzy inference system (ANFIS) algorithm was developed to predict the water quality index (WQI). Feed-forward neural network (FFNN) and K-nearest neighbors were applied to classify water quality. The dataset has eight significant parameters, but seven parameters were considered to show significant values. The proposed methodology was developed based on these statistical parameters. Prediction results demonstrated that the ANFIS model was superior for the prediction of WQI values. Nevertheless, the FFNN algorithm achieved the highest accuracy (100%) for water quality classification (WQC). Furthermore, the ANFIS model accurately predicted WQI, and the FFNN model showed superior robustness in classifying the WQC. In addition, the ANFIS model showed accuracy during the testing phase, with a regression coefficient of 96.17% for predicting WQI, and the FFNN model achieved the highest accuracy (100%) for WQC. This proposed method, using advanced artificial intelligence, can aid in water treatment and management.

Applying Probabilistic Water Quality Standards in River Basin Water Quality Optimization Models

1974 ◽  
Vol 9 (1) ◽  
pp. 25-29
Author(s):  
M. B. Bayer
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Minimum Cost ◽  
Water Quality Standards ◽  
Quality Optimization ◽  
Basin Water ◽  
Do Concentration ◽  
Water Quality Models

Abstract This paper describes a method of applying probabilistic DO (dissolved oxygen) and BOD (biochemical oxygen demand) standards in river basin water quality models. Maximum likelihood estimators for the DO and BOD concentrations variances for each reach are used to obtain a lower bound for BOD so that the probability of violating specified DO and BOD standards is less than Θ per cent in any reach. These boundary values for DO and BOD concentrations are incorporated into a nonlinear water quality optimization model for finding the minimum cost set of wastewater treatment plant efficiencies required to meet DO and BOD standards. The method also provides the minimum DO concentration and the maximum BOD concentration which may be expected to occur 1-Θ of the time for any reach.

The Research of Error Compensation for the 3D Surface Measurement

2012 ◽  
Vol 482-484 ◽  
pp. 2192-2196
Author(s):  
Yuan Tian ◽  
Zi Ma ◽  
Peng Li
Keyword(s):  
Measurement Error ◽  
Fuzzy Inference System ◽  
Error Compensation ◽  
Point Cloud ◽  
Fuzzy Inference ◽  
Surface Measurement ◽  
Anfis Model ◽  
Inference System ◽  
Surface Error ◽  
3D Surface

For improving precision of 3D surface measurement equipments, which are playing important role in reverse engineering, the Adaptive Network based Fuzzy Inference System (ANFIS) is developed to reconstruct 3D surface error, and the measurement error of point cloud is compensated by the presented 3D error ANFIS model. The precision of 3D surface measurement equipments has been improved noticeably

scholarly journals Classification of Gait Patterns in Patients with Neurodegenerative Disease Using Adaptive Neuro-Fuzzy Inference System

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Qiang Ye ◽  
Yi Xia ◽  
Zhiming Yao
Keyword(s):  
Motor Neurons ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Pso Algorithm ◽  
Anfis Model ◽  
Inference System ◽  
Gait Dynamics ◽  
Double Support ◽  
Adaptive Capabilities ◽  
Neuro Fuzzy

A common feature that is typical of the patients with neurodegenerative (ND) disease is the impairment of motor function, which can interrupt the pathway from cerebrum to the muscle and thus cause movement disorders. For patients with amyotrophic lateral sclerosis disease (ALS), the impairment is caused by the loss of motor neurons. While for patients with Parkinson’s disease (PD) and Huntington’s disease (HD), it is related to the basal ganglia dysfunction. Previously studies have demonstrated the usage of gait analysis in characterizing the ND patients for the purpose of disease management. However, most studies focus on extracting characteristic features that can differentiate ND gait from normal gait. Few studies have demonstrated the feasibility of modelling the nonlinear gait dynamics in characterizing the ND gait. Therefore, in this study, a novel approach based on an adaptive neuro-fuzzy inference system (ANFIS) is presented for identification of the gait of patients with ND disease. The proposed ANFIS model combines neural network adaptive capabilities and the fuzzy logic qualitative approach. Gait dynamics such as stride intervals, stance intervals, and double support intervals were used as the input variables to the model. The particle swarm optimization (PSO) algorithm was utilized to learn the parameters of the ANFIS model. The performance of the system was evaluated in terms of sensitivity, specificity, and accuracy using the leave-one-out cross-validation method. The competitive classification results on a dataset of 13 ALS patients, 15 PD patients, 20 HD patients, and 16 healthy control subjects indicated the effectiveness of our approach in representing the gait characteristics of ND patients.

ANFIS Model for Time Series Prediction

2013 ◽  
Vol 385-386 ◽  
pp. 1411-1414 ◽  
Author(s):  
Xue Bo Jin ◽  
Jiang Feng Wang ◽  
Hui Yan Zhang ◽  
Li Hong Cao
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Prediction Error ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Prediction Performance ◽  
Membership Functions ◽  
Adaptive Network ◽  
Anfis Model ◽  
Inference System

This paper describes an architecture of ANFIS (adaptive network based fuzzy inference system), to the prediction of chaotic time series, where the goal is to minimize the prediction error. We consider the stock data as the time series. This paper focuses on how the stock data affect the prediction performance. In the experiments we changed the number of data as input of the ANFIS model, the type of membership functions and the desired goal error, thereby increasing the complexity of the training.

The Chao Phraya River Basin: water quality and anthropogenic influences

2018 ◽  
Vol 19 (5) ◽  
pp. 1287-1294 ◽  
Author(s):  
Nuanchan Singkran ◽  
Pitchaya Anantawong ◽  
Naree Intharawichian ◽  
Karika Kunta
Keyword(s):  
Water Quality ◽  
Land Use ◽  
River Basin ◽  
River Mouth ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Paddy Fields ◽  
Coliform Bacteria ◽  
Wet Season ◽  
Chao Phraya River

Abstract Land use influences and trends in water quality parameters were determined for the Chao Phraya River, Thailand. Dissolved oxygen (DO), biochemical oxygen demand (BOD), and nitrate-nitrogen (NO3-N) showed significant trends (R2 ≥ 0.5) across the year, while total phosphorus (TP) and faecal coliform bacteria (FCB) showed significant trends only in the wet season. DO increased, but BOD, NO3-N, and TP decreased, from the lower section (river kilometres (rkm) 7–58 from the river mouth) through the middle section (rkm 58–143) to the upper section (rkm 143–379) of the river. Lead and mercury showed weak/no trends (R2 < 0.5). Based on the river section, major land use groups were a combination of urban and built-up areas (43%) and aquaculture (21%) in the lower river basin, paddy fields (56%) and urban and built-up areas (21%) in the middle river basin, and paddy fields (44%) and other agricultural areas (34%) in the upper river basin. Most water quality and land use attributes had significantly positive or negative correlations (at P ≤ 0.05) among each other. The river was in crisis because of high FCB concentrations. Serious measures are suggested to manage FCB and relevant human activities in the river basin.

scholarly journals Energy Consumption, Economic Growth, and CO2 Emissions in G20 Countries: Application of Adaptive Neuro-Fuzzy Inference System

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2771 ◽  
Author(s):  
Abbas Mardani ◽  
Dalia Streimikiene ◽  
Mehrbakhsh Nilashi ◽  
Daniel Arias Aranda ◽  
Nanthakumar Loganathan ◽  
...  
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Co2 Emissions ◽  
Fuzzy Inference System ◽  
Global Economy ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy

Understanding the relationships among CO2 emissions, energy consumption, and economic growth helps nations to develop energy sources and formulate energy policies in order to enhance sustainable development. The present research is aimed at developing a novel efficient model for analyzing the relationships amongst the three aforementioned indicators in G20 countries using an adaptive neuro-fuzzy inference system (ANFIS) model in the period from 1962 to 2016. In this regard, the ANFIS model has been used with prediction models using real data to predict CO2 emissions based on two important input indicators, energy consumption and economic growth. This study made use of the fuzzy rules through ANFIS to generalize the relationships of the input and output indicators in order to make a prediction of CO2 emissions. The experimental findings on a real-world dataset of World Development Indicators (WDI) revealed that the proposed model efficiently predicted the CO2 emissions based on energy consumption and economic growth. The direction of the interrelationship is highly important from the economic and energy policy-making perspectives for this international forum, as G20 countries are primarily focused on the governance of the global economy.

scholarly journals IDENTIFYING KEY FACTORS OF COMMUTER TRAIN SERVICE QUALITY:AN EMPIRICAL ANALYSIS FOR DHAKA CITY

2019 ◽  
Vol 31 (2) ◽  
Author(s):  
Anika Nowshin Mowrin ◽  
Md. Hadiuzzaman ◽  
Saurav Barua ◽  
Md. Mizanur Rahman
Keyword(s):  
Traffic Congestion ◽  
Fuzzy Inference ◽  
Confusion Matrix ◽  
Model Development ◽  
Model Performance ◽  
Total Sample ◽  
Dhaka City ◽  
Anfis Model ◽  
Regular User ◽  
Inference System

Commuter train is a viable alternative to road transport to ease the traffic congestion which requires appropriate planning by concerned authorities. The research is aimed to assess passengers’ perception about commuter train service running in areas near Dhaka city. An Adaptive Neuro Fuzzy Inference System (ANFIS) model has been developed to evaluate service quality (SQ) of commuter train. Field survey data has been conducted among 802 respondents who were the regular user of commuter train and 12 attributes have been selected for model development. ANFIS was developed by the training and then tested by 80% and 20% of the total sample respectively. After that, model performance has been evaluated by (i) Confusion Matrix (ii) Root Mean Square Error (RMSE) and attributes are ranked based on their relative importance. The proposed ANFIS model has 61.50% accuracy in training and 47.80% accuracy in testing.  From the results, it is found that 'Bogie condition', 'Cleanliness', ‘Female harassment’, 'Behavior of staff' and 'Toilet facility' are the most significant attributes. This indicates that some necessary measures should be taken immediately to recover the effects of these attributes to improve the SQ of commuter train. 

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