@article { author = {Bagheri Seyed Shakeri, Sajjad and Alipour, Abbas and maroofpour, saman and Hashemi, Seyed Moustafa}, title = {Development of Soft Computing Models in Estimating River Water Quality by Using Different Input Combination}, journal = {Journal of Geography and Planning}, volume = {22}, number = {63}, pages = {1-25}, year = {2018}, publisher = {University of Tabriz}, issn = {2008-8078}, eissn = {2717-3534}, doi = {4-1}, abstract = {Introduction The exploitation of natural water resources requires recognition of the quantity and, in particular, its quality. It is important to study the quality and quantity of flow in the river in order to evaluate its locative changes for its various uses. Usually the flow crossing the river is a source of water supply in various sectors of consumption, including drinking, agriculture and industry. Therefore, knowing the changes in the quality of river flow can have a significant impact on management and planning at harvest time and water consumption, especially drinking. Various studies have been done to predict and study water quality, but in terms of the quality of surface water, less attention has been paid to smart modeling. The superiority of smart models is determined in solving nonlinear and bulky problems that cannot be solved with high precision. Najah et.al (422: 2009) also emphasized the ability of neural networks to predict Malaysian ink's river water quality indices and the ability to estimate electrical conductivity (EC) and total dissolved solids (TDS) values and opacity in this basin. Kunwar et.al (95: 2009) has also used perceptron neural networks to model the quality parameters of the biological oxygen demand (BOD) and dissolved oxygen (DO) of Gottmy river in India and has emphasized its proper efficiency.The main objective of the present research is to construct a soft calculation model for estimating the salinity of the Nisa river flow at the site of the Yalkhary hydrometric station using various input scenarios which in areas such as the present study, there is the problem of data deficits, information, as well as lack of facilities and enough cost, can be done by using an estimation model with acceptable water quality accuracy.}, keywords = {Kerman province,Nesa river,soft computing,Water quality indices}, title_fa = {توسعه مدل‌های محاسبات نرم در برآورد کیفیت جریان رودخانه با استفاده از ترکیب ورودی‌های مختلف}, abstract_fa = {یکی از عوامل مهم توسعه در هر منطقه فراهم بودن منابع آب مناسب می‌باشد. در این راستا عـلاوه بـر کمیـت، توجـه بـه وضع کیفی آن نیز از اهمیت شایانی برخوردار است. هدف از این تحقیق کاربرد مدل‌های ANN، ANFIS-GP، ANFIS-SC و GEP در مدل‌سازی شاخص EC آب رودخانه‌ها با استفاده از ترکیب ورودی‌های مختلف است. به این منظور از اطلاعات و داده‌های 5 متغیر شامل TDS، SAR، PH، کلر و دبی آب رودخانه نساء (استان کرمان) در طول آماری 21 ساله (1390-1370) به‌عنوان شاخص‌های مؤثر بر شوری آب استفاده شد. کارایی مدل‌ها توسط معیار‌های آماری ضریب همبستگی (R)، ریشه میانگین مربعات خطا (RMSE) و میانگین خطای مطلق (MAE) مورد ارزیابی قرار گرفت. نتایج نشان داد که مدل GEP با سه ورودی دبی، TDS و PH  با داشتن کمترین RMSE  (679/19 میکروموس بر سانتی‌متر) و  MAE  (736/10 میکروموس بر سانتی‌متر) و بیشترین R2 (926/0) مناسب‌ترین مدل جهت پیش‌بینی EC  و به‌عنوان تکنیکی برتـر جهت پژوهش‌های بعدی و جایگزین مطالعات میدانی بـرای شـبیه‌سـازی تغییـرات شاخص EC آب رودخانه‌ها می‌باشد.}, keywords_fa = {استان کرمان,رودخانه نساء,شاخص‌های کیفیت آب,محاسبات نرم}, url = {https://geoplanning.tabrizu.ac.ir/article_7446.html}, eprint = {https://geoplanning.tabrizu.ac.ir/article_7446_ea7629829a4b2a02bfc0a4a6ba5c834c.pdf} }