Earth Sciences

Special Issue

Predicting Hydrological Drought Using Deep Learning Models

  • Submission Deadline: Aug. 01, 2024
  • Status: Open for Submission
  • Lead Guest Editor: Burak Kizilöz
About This Special Issue
Climate change negatively affects water resources, dams, and water quality worldwide. Therefore, water and sewerage administrations, experts, and academics have focused on predicting variables such as precipitation, temperature, and evaporation, or developing future scenarios. One of the most popular prediction approaches applied in recent years is deep learning methods. One of the crucial aspects that needs to be emphasized here is the detailed classification process. Drought indices such as SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation-Evapotranspiration Index) are commonly used for classification purposes. Comparing the prediction accuracies obtained by new classification water levels of a dam that has experienced a real drought event over a long-term period, new categorizing dam storage rates, and comparing these accuracy results with SPI, SPEI, and similar approaches can be beneficial in improving the prediction accuracy. Indeed, in the context of deep learning approaches, directing researchers towards classification outputs rather than numerical outputs can be more important. By focusing on classification outputs, researchers can obtain valuable insights into the categorization and identification of different patterns and classes within the data, which can be useful for decision-making and understanding the severity of drought conditions. With the development of these new classification methods, hydraulic droughts can be predicted more realistically. By utilizing advanced deep learning methods and incorporating relevant features and variables, these classification methods can provide more accurate and reliable predictions for hydraulic droughts. This can help administrations, as well as other stakeholders, in taking proactive measures to mitigate the impacts of droughts and effectively manage water resources.

Keywords:

  1. Deep Learning Methods
  2. Long Short-Term Memory Networks (LTSM)
  3. Convolutional Neural Networks (CNN)
  4. Drought Risk Assessment
  5. Water Scarcity
  6. Climate Adaptation Strategies
  7. Hydraulic Drought
  8. Standardized Precipitation Index (SPI)
  9. Standardized Precipitation and Evapotranspiration Index (SPEI)
  10. Drought Monitoring
Lead Guest Editor
  • Burak Kizilöz

    Department of Environmental Protection and Control, Kocaeli Water and Sewerage Administration, Kocaeli, Turkey

Guest Editors
  • Mücahit Opan

    Civil Engineering, Kocaeli, Kocaeli, Turkey

  • Eyüp Şişman

    Civil Engineerin/Hydraulic, Yıldız Technical Univ., İstanbul, Turkey

  • Ayfer Özdemir

    Republic of Turkey Ministry of Agriculture and Forestry, Antalya, Turkey

Manuscript Submission

Submitting your research to special issue is a simple and efficient process. Here are three submission options available:

Manuscript writing and formatting guidelines, please refer to Submission Guidelines.

1
Option 1: Quick Submission

Our Quick Submission tool provides a fast and uncomplicated way for you to submit your manuscripts.

Selected Special Issue:*
Special Issue Title Article Processing Charge
Predicting Hydrological Drought Using Deep Learning Models 970 USD
Article Title: *
Upload Manuscript:*
Upload

1. Allow only .doc, .docx and .tex format.
2. Files should not exceed 20MB.

Article Type: *
First Name:*
Last Name:*
Email Address:*

A follow-up email will be sent to the given email address within 3 working days.

Verification Code: *
Renew
2
Option 2: Online Submission

Submit your manuscript via our online submission system.

For unregistered users, please register at https://sso.sciencepg.com/register
For registered users, please log in at https://sso.sciencepg.com/login

Once you're logged in, click on "Submit to Special Issue" under the "Submission" section. Follow the system prompts to successfully complete the submission.

3
Option 3: Email Submission

You can submit your manuscript directly via email to submission@sciencepg.com

Please type "Predicting Hydrological Drought Using Deep Learning Models Manuscript Submission" as the subject of your email.