scholarly journals Application of Artificial Neural Network and Geographical Information System Models to Predict and Evaluate the Quality of Diyala River Water, Iraq

2019 ◽  
Vol 26 (1) ◽  
pp. 160-169
Author(s):  
Nawar Omran Al-Musawi ◽  
Fatima Muqdad Al-Rubaie
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Information System ◽  
Geographical Information System ◽  
Geographical Information ◽  
Total Alkalinity ◽  
Artificial Neural ◽  
Diyala River

This research discusses application Artificial Neural Network (ANN) and Geographical Information System (GIS) models on water quality of Diyala River using Water Quality Index (WQI). Fourteen water parameters were used for estimating WQI: pH, Temperature, Dissolved Oxygen, Orthophosphate, Nitrate, Calcium, Magnesium, Total Hardness, Sodium, Sulphate, Chloride, Total Dissolved Solids, Electrical Conductivity and Total Alkalinity. These parameters were provided from the Water Resources Ministryfrom seven stations along the river for the period 2011 to 2016. The results of WQI analysis revealed that Diyala River is good to poor at the north of Diyala province while it is poor to very polluted at the south of Baghdad City. The selected parameters were subjected to Kruskal-Wallis test for detecting factors contributing to the degradation of water quality and for eliminating independent variables that exhibit the highest contribution in p-value. The analysis of results revealed that ANN model was good in predicting the WQI. The confusion matrix for Artificial Neural Model (NNM) gave almost 96% for training, 85.7% for testing and 100% for holdout. In relation to GIS, six color maps of the river have been constructed to give clear images of the water quality along the river.

Implementasi Artificial Neural Network Dalam Memprediksi Pembiayaan Bermasalah Pada BMT Al Munawwarah

Liquidity ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 7-18
Author(s):  
Siti Chodijah ◽  
Saiful Anwar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Information System ◽  
Artifical Neural Network ◽  
Design Software ◽  
Artificial Neural ◽  
Hidden Layer

The percentage of non-performing financing in BMT Al Munawwarah at 2016 exactly high for every month, so should be taken a method to predict the quality of financing before filing a customer applicant approved. An Artifical Neural Network (ANN) is processing of information system has characteristic similar biology neural network, ANN used to predict because the good approachment ability toward unlinear. This research attempts to design software to predict the quality of financing with the ANN method. Based on the results of training with training datasets 276 data and validation datasets 91 data, using architecture with 1 hidden layer and 164 neurons, iteration 2000, and retrain 10 times, produce results the accuration of application 82%. With the test datasets 91 data, applications can recognize the test datasets about 75 data. Based on these results, ANN can be used to predict the quality of financing.

scholarly journals WATER QUALITY CLASSIFICATION BY ARTIFICIAL NEURAL NETWORK - A CASE STUDY OF DONG NAI RIVER, VIETNAM

2018 ◽  
Vol 55 (4C) ◽  
pp. 297
Author(s):  
Nguyen Hien Than
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Urban Areas ◽  
Fuzzy Comprehensive Evaluation ◽  
Neural Network Models ◽  
Quality Classification ◽  
Water Quality Classification ◽  
Artificial Neural

The Dong Nai River is the main source of supplied water for Ho Chi Minh City, Dong Nai, Binh Duong province and other areas. However, the water quality state of the Dong Nai River has been heavily pressured by discharged sources from urban areas, industrial zones, agricultural, domestic activities, etc. In this paper, the authors employed the artificial neural network model (ANNs) to classify water quality of Dong Nai River that apply a new tool to assess water quality in Vietnam. The monitoring data were used for eight years from 2007 to 2014 with 23 monitoring stations. Two neural network models including a multi-layer perceptron (MLPNN) and a generalized regression network (GRNN) were employed to classify water quality of the Dong Nai River. The results of the study showed that GRNN and MLPNN classified excellently water quality. Optimal structure of the MLPNN was H8I4O1 with model error about 0.1268 while the GRNN was error about 0.00001615. Comparing the result of water quality classification between the ANNs and the fuzzy comprehensive evaluation indicated that they were in close agreement with the respective values (the accurate rate of GRNN 100% and 98,5 % of MLPNN).

scholarly journals Treatability influence of municipal sewage effluent on surface water quality assessment based on Nemerow pollution index using an artificial neural network

2021 ◽  
Vol 877 (1) ◽  
pp. 012008
Author(s):  
R Mohammed ◽  
B Al-Obaidi
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Quality Assessment ◽  
Oxygen Demand ◽  
Pollution Index ◽  
Water Quality Assessment ◽  
Artificial Neural ◽  
The Impact

Abstract Assessing water quality provides a scientific foundation for the development and management of water resources. The objective of the research is to evaluate the impact treated effluent from North Rustumiyia wastewater treatment plant (WWTP) on the quality of Diyala river. The model of the artificial neural network (ANN) and factor analysis (FA) based on Nemerow pollution index (NPI). To define important water quality parameters for North Al-Rustumiyia for the line(F2), the Nemerow Pollution Index was introduced. The most important parameters of assessment of water variation quality of wastewater were the parameter used in the model: biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspension solids (SS), chloride, cl, hydrogen ion concentration, pH, sulfate, SO4-2, nitrate, NO3- and phosphate, PO4-3. Taking these criteria into account, samples of water from the sampling sites were graded as C, indicating the pollutant of the waste treatment. Then the water quality map using neural network model was based on the results of water quality assessment. The results showed that the model North Al-Rustumiyia for line F2 was more efficient and R2 was 0.965 with the impotence parameter was chloride (CL).

scholarly journals Prediction of Water Quality based on artificial neural network

2021 ◽  
Vol 1738 ◽  
pp. 012066
Author(s):  
Yingjia Wu ◽  
Rong Ling ◽  
Jixian Zhou ◽  
Mengxin Zhang ◽  
Wei Gao
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document