Hourly river flow forecasting: application of emotional neural network versus multiple machine learning paradigms

Monitoring hourly river flows is indispensable for flood forecasting and disaster risk management. The objective of the present study is to develop a suite of hourly river flow forecasting models for the Albert river, located in Queensland, Australia using various machine learning (ML) based models including a relatively new and novel artificial intelligent modeling technique known as emotional neural network (ENN). Hourly river flow data for the period 2011–2014 is employed for the development and evaluation of the predictive models. The performance of the ENN model in forecasting hourly stage river flow is compared with other well-established ML-based models using a number of statistical metrics and graphical evaluation methods. The ENN showed an outstanding performance in terms of their forecasting accuracies, in comparison with other ML models. In general, the results clearly advocate the ENN as a promising artificial intelligence technique for accurate forecasting of hourly river flow in the form of real-time

An Enhanced Multioperator Runge–Kutta Algorithm for Optimizing Complex Water Engineering Problems

Water engineering problems are typically nonlinear, multivariable, and multimodal optimization problems. Accurate water engineering problem optimization helps predict these systems’ performance. This paper proposes a novel optimization algorithm named enhanced multioperator Runge–Kutta optimization (EMRUN) to accurately solve different types of water engineering problems. The EMRUN’s novelty is focused mainly on enhancing the exploration stage, utilizing the Runge–Kutta search mechanism (RK-SM), the covariance matrix adaptation evolution strategy (CMA-ES) techniques, and improving the exploitation stage by using the enhanced solution quality (IESQ) and sequential quadratic programming (SQP) methods. In addition to that, adaptive parameters were included to improve the stability of these two stages. The superior performance of EMRUN is initially tested against a set of CEC-17 benchmark functions. Afterward, the proposed algorithm extracts parameters from an eight-parameter Muskingum model. Finally, the EMRUM is applied to a practical hydropower multireservoir system. The experimental findings show that EMRUN performs much better than advanced optimization approaches. Furthermore, the EMRUN has demonstrated the ability to converge up to 99.99% of the global solution. According to the findings, the suggested method is a competitive algorithm that should be considered in optimizing water engineering problems

Empirical model for the assessment of climate change impacts on spatial pattern of water availability in Nigeria

Rising temperatures and changing rainfall patterns due to global warming would affect sustainability in water resources in many regions. This change would impact several sectors, particularly the agricultural and water resources. The major objective of the present study is to model the impacts of climate change on spatial variability in water sustainability of Nigeria. Gauge based gridded rainfall data of global precipitation climatology centre (GPCC) and temperature data of climate research unit (CRU) for the period 1901–2010 and total water storage (TWS) anomaly data of Gravity Recovery and Climate Experiment (GRACE) for the period 2002–2016 were used for this purpose. The concept of reliability-resiliency-vulnerability was used for the assessment of sustainability in water resources. Machine learning models were used for the development of empirical models for the simulation of TWS from GPCC rainfall and CRU temperature. Finally, the multi-model ensemble mean projections of rainfall and temperature of four GCMs namely MRI-CGCM3, HadGEM2-ES, CSIRO-Mk3-6-0 and CESM1-CAM5 were used in the model for the assessment of climate change impact on water sustainability. The results revealed the declination of TWS in Nigeria up to -12 m during the rainy periods in some parts. Spatial assessment of the changes in TWS for the future shows the northeast, southeast and south-south parts would mostly experience decreases in TWS. Water sustainability will be low in these areas and some other parts of the country for the future