Dear Colleagues,

In the face of climate change and population growth in many parts of the world, we need appropriate tools that can assist in dealing with the difficulties introduced by the increasing complexity of water problems. Flooding and drought hazards cause numerous economic and life losses in the present changing climate and environment. It is, therefore, important to continue developing and improving our knowledge in the field of flood vulnerability assessment and hazard alleviation. Water resource management at the catchment level is a scientific discipline with great environmental importance. It is a multidisciplinary issue, which has prevailed from the cooperation of a wide range of scientists, such as engineers, Earth scientists, agronomists, environmentalists, biologists, and economists. The target is the optimal distribution of limited water resources and the preservation of acceptable levels of water quality, in such a way that all the users’ needs in domestic, agricultural, industrial, and ecological uses are satisfied with the least controversy and conflict. In order to achieve operational and efficient water management, we need to have reliable methodologies. This Special Issue will feature the latest advances and developments in operational hydrologic forecasts and water resource management. The focus is centered on advanced machine learning and deep learning methods for operational hydrologic forecasting for optimal water resource management. The computational power available today allows us to tackle simulation challenges in hydraulic and hydrological modeling at different scales that were impossible a few decades ago. However, even in the current situation, the time needed for these simulations is inadequate for many scientific and engineering applications, such as decision support systems, flood warning systems, design or optimization of hydraulic structures, calibration of model parameters, uncertainty quantification, real-time model-based control, etc.

To address these issues, the development of fast computing models to increase the simulation speed seems to be a promising strategy: It does not require a huge investment in new hardware and software, and the same tools can be used to solve very different problems. The main themes of this Special Issue include but are not limited to the following: 

• Application of advanced machine learning models including deep learning methods for precise hydrologic forecasting (modeling rainfall, runoff, sediment, surface water and groundwater quality, lake level, water temperature, reservoir inflow, evaporation, evapotranspiration etc.);
• Utilization of advanced machine learning models with ensemble models for solving water resource problems;
• Spatial and temporal modeling of hydrological variable with aid of advanced computing models;
• Coupling of data preprocessing techniques with machine learning methods to capture noise and nonlinear of hydrological variables;
• Use and development of novel metaheuristic algorithms with machine learning methods to enhance their computing abilities.

Prof. Dr. Ozgur Kisi
Dr. Rana Muhammad Adnan
Guest Editors

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• metaheuristic algorithms
• data mining and deep learning
• prediction
• modeling
• optimization
• hybridization
• soft computing
• streamflow
• rainfall–runoff
• evaporation, evapotranspiration
• water resource management
• conservation and sustainability

1. Title: Hybrid Machine Learning Approach in pan evaporation modeling

Corresponding Author: Ozgur Kisi 

The main goal of the study was to predict epan using hybrid machine learning and metaheuristic algorithms to automatically tune the parameters, namely the Random Forest (RF), Support Vector Machines (SVM), the Grey Wolf Algorithm (GWO), and the Sparrow Search Algorithm (SSA).

Estimated submission date: 15 October 2022

2. Title: Improved artificial neural network usage for Eto prediction 

Corresponding Author: Rana Muhammad Adnan 

This study will propose a robust artificial intelligence (AI) model based on the social behaviour of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for modelling.

Estimated submission date: 30 October 2022

3. Title: Novel integrated approaches for predicting the streamflow

Corresponding Author: Ozgur Kisi 

The cascade forward neural network (CFNN) is selected to predict streamflow. Three optimization algorithms were considered to optimize the CFNN model aiming to improve the accuracy of the CFNN model in predicting the compressibility behavior of clay, including grey wolf optimization (GWO), hunger games search (HGS), and genetic algorithm (GA), named as GWO-CFNN, HGS-CFNN, and GA-CFNN model.

Estimated submission date: 15 November 2022

4. Title: State-of-the-art techniques application in predicting suspended sediment

Corresponding Author: Abolfazl Jaafari 

The radial basis function neural network (RBFNN) model will applied to predict. In order to optimize the RBFNN model, the brainstorm optimization (BSO) algorithm will applied to train the RBFNN model, named as BSO-RBFNN model.

Estimated submission date: 30 November 2022

5. Title: Rainfall-Runoff Modeling using a robust hybrid computational model

Corresponding Author: Rana Muhammad Adnan 

This study will propose a robust hybrid computational model based on gene expression programming (GEP) and particle swarm optimization (PSO), called GEP-PSO, to model rainfall runoff process.

Estimated submission date: 15 December 2022