Complexity

Solving Complex Hydrological Processes using Advanced Artificial Intelligence Models


Publishing date
01 Apr 2021
Status
Closed
Submission deadline
27 Nov 2020

1Ton Duc Thang University, Ho Chi Minh City, Vietnam

2Universiti Teknologi Malaysia, Johor, Malaysia

3Ilia State University, Tbilisi, Georgia

This issue is now closed for submissions.
More articles will be published in the near future.

Solving Complex Hydrological Processes using Advanced Artificial Intelligence Models

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Modelling the complexity of hydrological processes is essential to understand the processes involved with different components of hydrological cycles and their changes due to anthropogenic interventions. Hydrological processes control the water movement in the hydrological cycle and thus determine all forms of hydrological processes (e.g., evapotranspiration, precipitation, groundwater recharge, and river flow). These are considered to be challenging engineering problems owing to their complexity.

The development of computer aid models can be used for the planning and management of water resources, assessment of hydro-climatic hazard risk, evaluation of agricultural potential, understanding ecological distribution, etc. Physically-based models are most widely used for the simulation of hydrological processes where analytical or numerical methods are generally used. The physically-based models need a large amount of information for reliable modelling of hydrological processes which is often compromised through simplification of the real-word system. Therefore, such models often fail to provide reliable results. Different statistical models based on the relationship between data among the different components of the hydrological cycle have gained popularity in recent years due to their higher capability to simulate different hydrological processes. The relationship among various components responsible for a hydrological process is always non-linear, non-stationary, and stochastic. In many cases, the relationship is extremely non-linear and highly stochastic and is not possible to solve using conventional statistical methods. The artificial intelligence (AI) models and their advanced versions have the capability to model highly non-linear and stochastic phenomena and therefore it has been widely used in recent years for successful monitoring, analysis and forecasting of different hydrological processes. The AI models have been evidenced to demonstrate an excellent advanced computer aid machine learning model. AI models can be used for the construction of predictive models for decision support in water resources management, hydrological hazard risk reduction, and environmental management.

The aim of this Special Issue is to welcome novel research and review articles on the applications of primitive and modern-day soft computing modelling strategies for the simulation of hydrological processes. The submitted work should advance the knowledge of machine learning to describe, understand, analyse, model, and forecast hydrological processes.

Potential topics include but are not limited to the following:

  • Advanced artificial intelligence models in hydrology
  • Hydrological process simulation
  • Climate change
  • Decision tools and agent-based models
  • Uncertainty analysis in hydrology
  • Time series forecasting in hydrology
  • Watershed monitoring and sustainability
  • Hybrid machine learning models in hydrology
  • Environmental engineering in hydrology
  • Hydro-climatic hazard risk management
  • Water resource planning and management
  • Non-linear, non-stationary, and stochastic problems in hydrology

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 6611848
  • - Research Article

Using Hybrid Wavelet-Exponential Smoothing Approach for Streamflow Modeling

Vahid Nourani | Hessam Najafi | ... | Hitoshi Tanaka
  • Special Issue
  • - Volume 2021
  • - Article ID 6631564
  • - Research Article

Bayesian Regularized Neural Network Model Development for Predicting Daily Rainfall from Sea Level Pressure Data: Investigation on Solving Complex Hydrology Problem

Lu Ye | Saadya Fahad Jabbar | ... | Mou Leong Tan
  • Special Issue
  • - Volume 2021
  • - Article ID 6643472
  • - Research Article

Improvement in Explicit Prediction of Water Quality Using Wavelet-Based LSSVR and M5pRT

Rashmi Bhardwaj | Aashima Bangia
  • Special Issue
  • - Volume 2021
  • - Article ID 6627011
  • - Research Article

Assessment of Artificial Intelligence Models for Developing Single-Value and Loop Rating Curves

Majid Niazkar | Mohammad Zakwan
  • Special Issue
  • - Volume 2021
  • - Article ID 6610228
  • - Research Article

Forecasting Different Types of Droughts Simultaneously Using Multivariate Standardized Precipitation Index (MSPI), MLP Neural Network, and Imperialistic Competitive Algorithm (ICA)

Pouya Aghelpour | Vahid Varshavian
  • Special Issue
  • - Volume 2020
  • - Article ID 8844367
  • - Research Article

Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting

Hai Tao | Ali Omran Al-Sulttani | ... | Reham R. Mostafa
  • Special Issue
  • - Volume 2020
  • - Article ID 7146593
  • - Review Article

Comparison of Statistical, Graphical, and Wavelet Transform Analyses for Rainfall Trends and Patterns in Badulu Oya Catchment, Sri Lanka

Ashika M. Ruwangika | Anushka Perera | Upaka Rathnayake
Complexity
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Acceptance rate43%
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Acceptance to publication35 days
CiteScore3.300
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