Flood Routing in River Reaches Using a Three-Parameter Muskingum Model Coupled with an Improved Bat Algorithm

In this study, a coupled water-energy balance equation at an arbitrary time scale was proposed as an extension of the Budyko hypothesis. The second mixed partial derivative was selected to represent the magnitude of the interaction. The extended hydrological sensitivity method was used to quantitatively evaluate the impacts of climate change, anthropogenic activities, and their interaction on dry season runoff in the Lhasa River. In addition, an ABCD model, which is a monthly hydrological model included a snowmelt module, was used to calculate the change in soil water and groundwater storage. The Mann-Kendall test, Spearman's test, dynamic linear model (DLM), and Yamamoto's method were used to identify trends and change points in hydro-climatic variables from 1956-2016. The results found that dry season runoff increased non-significantly over the last 61 years. Climate change, which caused an increase in dry season runoff, was the dominant factor, followed by anthropogenic activities and their interaction, which led to varying degrees of decrease. This study concluded that the methods tested here performed well in quantifying the relative impacts of climate change, anthropogenic activities, and their interaction on dry season runoff change.

Section: Genetic Algorithm Mehtodmentioning

confidence: 99%

Attribution Analysis of Dry Season Runoff in the Lhasa River Using an Extended Hydrological Sensitivity Method and a Hydrological Model

Mei

Chen

et al. 2019

“…Improving the Muskingum Routing method using various optimization methods such as hybrid bat-swarm algorithm [9], improved bat algorithm [10], and Wolf Pack Algorithm [11], or combined with a particle filter-based assimilation model [12] for streamflow forecasts; 5.…”

mentioning

confidence: 99%

Building an Intelligent Hydroinformatics Integration Platform for Regional Flood Inundation Warning Systems

Chang

Yang

et al. 2018

Flood disasters have had a great impact on city development. Early flood warning systems (EFWS) are promising countermeasures against flood hazards and losses. Machine learning (ML) is the kernel for building a satisfactory EFWS. This paper first summarizes the ML methods proposed in this special issue for flood forecasts and their significant advantages. Then, it develops an intelligent hydroinformatics integration platform (IHIP) to derive a user-friendly web interface system through the state-of-the-art machine learning, visualization and system developing techniques for improving online forecast capability and flood risk management. The holistic framework of the IHIP includes five layers (data access, data integration, servicer, functional subsystem, and end-user application) and one database for effectively dealing with flood disasters. The IHIP provides real-time flood-related data, such as rainfall and multi-step-ahead regional flood inundation maps. The interface of Google Maps fused into the IHIP significantly removes the obstacles for users to access this system, helps communities in making better-informed decisions about the occurrence of floods, and alerts communities in advance. The IHIP has been implemented in the Tainan City of Taiwan as the study case. The modular design and adaptive structure of the IHIP could be applied with similar efforts to other cities of interest for assisting the authorities in flood risk management.

“…The Bat algorithm is known to be an effective tool for optimization problems. Previous research has shown that it is highly capable of dealing with different issues such as water resource management, energy generation and nonlinear mathematical functions [32,33]. The bats can differentiate the obstacles from food based on sound.…”

Section: Bat Algorithm (Ba)mentioning

confidence: 99%

“…In fact, they generate a loud sound and then receive sound echoes at a specific frequency. Three main assumptions can be made for the BA [33]:…”

Section: Bat Algorithm (Ba)mentioning

confidence: 99%

Assessing the Predictability of an Improved ANFIS Model for Monthly Streamflow Using Lagged Climate Indices as Predictors

Ehteram

Afan

Dianatikhah

et al. 2019

Self Cite

The current study investigates the effect of a large climate index, such as NINO3, NINO3.4, NINO4 and PDO, on the monthly stream flow in the Aydoughmoush basin (Iran) based on an improved Adaptive Neuro Fuzzy Inference System (ANFIS) during 1987–2007. The bat algorithm (BA), particle swarm optimization (PSO) and genetic algorithm (GA) were used to obtain the ANFIS parameter for the best ANFIS structure. Principal component analysis (PCA) and Varex rotation were used to decrease the number of effective components needed for the streamflow simulation. The results showed that the large climate index with six-month lag times had the best performance, and three components (PCA1, PCA2 and PCA3) were used to simulate the monthly streamflow. The results indicated that the ANFIS-BA had better results than the ANFIS-PSO and ANFIS-GA, with a root mean square error (RMSE) 25% and 30% less than the ANFIS-PSO and ANFIS-GA, respectively. In addition, the linear error in probability space (LEPS) score for the ANFIS-BA, based on the average values for the different months, was less than the ANFIS-PSO and ANFIS-GA. Furthermore, the uncertainty values for the different ANFIS models were used and the results indicated that the monthly simulated streamflow by the ANFIS was computed well at the 95% confidence level. It can be seen that the average streamflow for the summer season is 75 m3/s, so that the stream flow for summer, based on climate indexes, is more than that in other seasons.