Neural Networks in Water Resources Management

2001 ◽  
Author(s):  
Luís F. S. Fernandes ◽  
Naim Haie
Keyword(s):  
Neural Networks ◽  
Water Resources ◽  
Water Resources Management ◽  
Resources Management

scholarly journals Deep Learning in Water Resources Management: Τhe Case Study of Kastoria Lake in Greece

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3364
Author(s):  
Lina Karamoutsou ◽  
Aris Psilovikos
Keyword(s):  
Neural Networks ◽  
Water Resources ◽  
Dissolved Oxygen ◽  
Water Resources Management ◽  
Deep Neural Networks ◽  
High Efficiency ◽  
Quality Parameter ◽  
Good Choice ◽  
Resources Management ◽  
Chl A

The effects of climate change on water resources management have drawn worldwide attention. Water quality predictions that are both reliable and precise are critical for an effective water resources management. Although nonlinear biological and chemical processes occurring in a lake make prediction complex, advanced techniques are needed to develop reliable models and effective management systems. Artificial intelligence (AI) is one of the most recent methods for modeling complex structures. The applications of machine learning (ML), as a part of AI, in hydrology and water resources management have been increasing in recent years. In this paper, the ability of deep neural networks (DNNs) to predict the quality parameter of dissolved oxygen (DO), in Lake Kastoria, Greece, is tested. The available dataset from 11 November 2015, to 15 March 2018, on an hourly basis, from four telemetric stations located in the study area consists of (1) Chl-a (μg/L), (2) pH, (3) temperature—Tw (°C), (4) conductivity (μS/cm), (5) turbidity (NTU), (6) ammonia (NH4, mg/L), (7) nitrate nitrogen (N–NO3, mg/L), and (8) dissolved oxygen (DO) (mg/L). Feed-forward deep neural networks (FF-DNNs) of DO, with different structures, are tested for all stations. All the well-trained DNNs give satisfactory results. The optimal selected FF-DNNs of DO for each station with a high efficiency (NSE > 0.89 for optimal selected structures/station) constitute a good choice for modeling dissolved oxygen. Moreover, they provide information in real time and comprise a powerful decision support system (DSS) for preventing accidental and emergency conditions that may arise from both natural and anthropogenic hazards.

KERNEL METHODS AND NEURAL NETWORKS FOR WATER RESOURCES MANAGEMENT

2010 ◽  
Vol 9 (2) ◽  
pp. 181-187 ◽  
Author(s):  
Lazaros S. Iliadis ◽  
Stefanos I. Spartalis ◽  
Stavros Tachos
Keyword(s):  
Neural Networks ◽  
Water Resources ◽  
Water Resources Management ◽  
Kernel Methods ◽  
Resources Management

ECOLOGY AND SUSTAINABLE DEVELOPMENT IN LOCAL BUSINESS:SIMULATION OF LOW IMPACT DEVELOPMENT IN SURAKARTA REGENCY

2018 ◽  
Vol 4 (1) ◽  
pp. 32-38
Author(s):  
Bhimo Rizky Samudro ◽  
Yogi Pasca Pratama
Keyword(s):  
Water Resources ◽  
Water Resources Management ◽  
River Flow ◽  
Low Impact Development ◽  
Development Model ◽  
Business Activity ◽  
Resources Management ◽  
Central Java ◽  
Specific Culture ◽  
Central Java Province

This paper will describe the function of water resources to support business activities in Surakarta regency, Central Java province. Surakarta is a business city in Central Java province with small business enterprises and specific culture. This city has a famous river with the name is Bengawan Solo. Bengawan Solo is a River Flow Regional (RFR) to support business activities in Surakarta regency. Concious with the function, societies and local government in Surakarta must to manage the sustainability of River Flow Regional (RFR) Bengawan Solo. It is important to manage the sustainability of business activity in Surakarta regency.   According to the condition in Surakarta regency, this paper will explain how the simulation of Low Impact Development Model in Surakarta regency. Low Impact Development is a model that can manage and evaluate sustainability of water resources in River Flow Regional (RFR). Low Impact Development can analys goals, structures, and process water resources management. The system can also evaluate results and impacts of water resources management. From this study, we hope that Low Impact Development can manage water resources in River Flow Regional (RFR) Bengawan Solo.  

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