scholarly journals Assessing Groundwater Vulnerability Potential using Modified DRASTIC in Ajabshir Plain, NW of Iran

2021 ◽  
Author(s):  
Asghar Ashari Moghaddam ◽  
Sorayya Nouri sangarab ◽  
Ali Kadkhodaie Ilkhchi
Keyword(s):  
Fuzzy Inference ◽  
Pollution Index ◽  
Primary Source ◽  
Groundwater Vulnerability ◽  
Nitrate Pollution ◽  
Inference System ◽  
Drastic Model ◽  
Northwest Of Iran ◽  
Optimized Model ◽  
Agricultural Areas

Abstract The vulnerability of groundwater, as the primary source of water for human survival, should be assessed for the purpose of pollution management. The Ajabshir plain, one of the major agricultural areas in the northwest of Iran, is always prone to pollution. Therefore, to prevent the increase in pollution, it is necessary to determine the polluting factors and areas prone to groundwater pollution. In this study, by modifying the DRASTIC method using the land-use layer, called DRASTICL, vulnerable areas and pollution index were mapped. To ensure dealing with the uncertainty of the parameters, the DRASTICL model was optimized utilizing the Sugeno-type fuzzy inference system. The models were validated based on nitrate pollution. The correlation of DRASTICL and its optimized model with the nitrate pollution are 0.31 and 0.80, respectively. The results of this study show that integrating the DRASTIC model and fuzzy knowledge is an instrumental way for assessment of vulnerability potential.

A Mamdani Adaptive Neural Fuzzy Inference System for Improvement of Groundwater Vulnerability

Ground Water ◽  
2018 ◽  
Vol 56 (6) ◽  
pp. 978-985 ◽  
Author(s):  
Belgacem Agoubi ◽  
Radhia Dabbaghi ◽  
Adel Kharroubi
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Groundwater Vulnerability ◽  
Inference System ◽  
Neural Fuzzy

scholarly journals Prediction of gas velocity in two-phase flow using developed fuzzy logic system with differential evolution algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Samyar Zabihi ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Azam Marjani ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Fuzzy Inference ◽  
Differential Evolution Algorithm ◽  
Gas Velocity ◽  
Two Phase ◽  
Inference System ◽  
De Algorithm ◽  
Optimized Model ◽  
Evolution Algorithm ◽  
Logic System

AbstractIn this investigation, differential evolution (DE) algorithm with the fuzzy inference system (FIS) are combined and the DE algorithm is employed in FIS training process. Considered data in this study were extracted from simulation of a 2D two-phase reactor in which gas was sparged from bottom of reactor, and the injected gas velocities were between 0.05 to 0.11 m/s. After doing a couple of training by making some changes in DE parameters and FIS parameters, the greatest percentage of FIS capacity was achieved. By applying the optimized model, the gas phase velocity in x direction inside the reactor was predicted when the injected gas velocity was 0.08 m/s.

scholarly journals Modelling nitrate concentration of groundwater using adaptive neural-based fuzzy inference system

2012 ◽  
Vol 7 (No. 2) ◽  
pp. 73-83 ◽  
Author(s):  
S.F. Mousavi ◽  
M.J. Amiri
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Nitrate Concentration ◽  
Sodium Absorption ◽  
Inference System ◽  
Sodium Absorption Ratio ◽  
Isfahan Province ◽  
High Nitrate Concentration ◽  
Agricultural Areas ◽  
Observation Wells

High nitrate concentration in groundwater is a major problem in agricultural areas in Iran. Nitrate pollution in groundwater of the particular regions in Isfahan province of Iran has been investigated. The objective of this study was to evaluate the performance of Adaptive Neural-Based Fuzzy Inference System (ANFIS) for estimating the nitrate concentration. In this research, 175 observation wells were selected and nitrate, potassium, magnesium, sodium, chloride, bicarbonate, sulphate, calcium and hardness were determined in groundwater samples for five consecutive months. Electrical conductivity (EC) and pH were also measured and the sodium absorption ratio (SAR) was calculated. The five-month average of bicarbonate, hardness, EC, calcium and magnesium are taken as the input data and the nitrate concentration as the output data. Based on the obtained structures, four ANFIS models were tested against the measured nitrate concentrations to assess the accuracy of each model. The results showed that ANFIS1 was the most accurate (RMSE = 1.17 and R<sup>2</sup> = 0.93) and ANFIS4 was the worst (RMSE = 2.94 and R<sup>2</sup> = 0.68) for estimating the nitrate concentration. In ranking the models, ANFIS2 and ANFIS3 ranked the second and third, respectively. The results showed that all ANFIS models underestimated the nitrate concentration. In general, the ANFIS1 model is recommendable for prediction of nitrate level in groundwater of the studied region.

scholarly journals Drastic method to map groundwater vulnerability to pollution using nitrate measurements in agricultural areas

2017 ◽  
Vol 47 (2) ◽  
pp. 981 ◽  
Author(s):  
T. Samara ◽  
G. Yoxas
Keyword(s):  
Land Use ◽  
Nitrate Concentration ◽  
Groundwater Vulnerability ◽  
Original Method ◽  
Northwestern Part ◽  
Drastic Model ◽  
Agricultural Areas ◽  
The Relationship ◽  
Better Than

DRASTIC model has been used to map groundwater vulnerability to pollution in many areas. Since this method is used in different places without any changes, it cannot consider the effects of pollution type and characteristics. Therefore, the method needs to be calibrated and corrected for that aquifer and specific land use. In this research, by correcting the rates of DRASTIC parameters, one can assess the vulnerability potential to pollution more accurately. The new rates were computed using the relationships between DRASTIC INDEX (DI) corresponding to land use and to nitrate concentration in groundwater. The proposed methodology was applied in deltaic region of alluvial aquifer of Volinaios catchment located in the northwestern part of Peloponnesus. In order to determine the quality of the ground waters, either for watering or irrigating purposes, in the study area, a sampling was made. Correlation was used to find the relationship between the index and measured pollution in each point and therefore, to modify the rates. The results showed that the modified DRASTIC is better than the original method for nonpoint source pollution in agricultural areas.

scholarly journals Modified DRASTIC model for groundwater vulnerability to nitrate contamination in the Dagujia River Basin, China

2021 ◽  
Author(s):  
Aihua Wei ◽  
Pan Bi ◽  
Jie Guo ◽  
Shuai Lu ◽  
Duo Li
Keyword(s):  
Land Use ◽  
Mean Squared Error ◽  
Groundwater Vulnerability ◽  
Nitrate Pollution ◽  
Nitrate Contamination ◽  
Support Vector ◽  
Drastic Model ◽  
Rapid Economic Growth ◽  
Groundwater Samples ◽  
Over Exploitation

Abstract Due to rapid economic growth and over-exploitation of groundwater, nitrate pollution in groundwater has become very serious. The main objective of this study is to modify the DRASTIC model to identify groundwater vulnerability to nitrate pollution. The DRASTIC model was firstly used to analyze the intrinsic vulnerability. The DRASTIC model with the inclusion of a land-use factor (DRASTIC-LU) was put forward to map the specific vulnerability of groundwater. Furthermore, the Support Vector Machine (SVM) was introduced to avoid the drawback of the overlay and index methods, and the improved integrated models of DRASTIC + SVM and DRASTIC-LU + SVM were built. Moreover, 103 groundwater samples were collected for building and validating the models. The Root Mean Squared Error (RMSE) of DRASTIC, DRASTIC-LU, DRASTIC + SVM, and DRASTIC-LU + SVM was found to be 0.853, 0.755, 0.631, and 0.502, respectively. The model DRASTIC-LU was more precise than the original one. The results also showed that the integrated model using SVM exhibited better correlation between the vulnerability value and the nitrate pollution. The study indicated that the modified models including the land-use factor as well as SVM in the DRASTIC model were more suitable to assess the groundwater vulnerability to nitrate.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

Sign in / Sign up

Export Citation Format

Share Document