Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Zaher Mundher Yaseen,Hossam Faris,Nadhir Al-Ansari

doi:10.1155/2020/8206245

Zaher Mundher Yaseen, Hossam Faris + Show 1 more

Open Access

https://doi.org/10.1155/2020/8206245

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Abstract

The capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. The classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem. This current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow. Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study. Different input combinations are applied for constructing the predictive models based on antecedent values. The results are evaluated based on several statistical measures and graphical presentations. The river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models. Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model. In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq.

Highlights

Due to the wide range of space and time variabilities, river flow modeling has been a critical problem in water management and hydrology [1]. e operation of many water resources system rule curves needs monthly river flow forecasts [2]
Over the past couple decades, massive attention has been focused on hydrological time series modeling, for river flow process. is is owing to the need of an accurate and reliable intelligent model that can be implemented in real practice
A novel hybrid intelligent predictive model was developed to be implemented on a regression hydrological problem

Summary

Introduction

Due to the wide range of space and time variabilities, river flow modeling has been a critical problem in water management and hydrology [1]. e operation of many water resources system rule curves needs monthly river flow forecasts [2]. E findings of the improved ELM model demonstrated a noticeable prediction performance In another attempt, Yaseen et al developed an ELM model to forecast the monthly scale river flow located in semiarid environment in Iraq. SSA has shown an impressive performance in optimizing various complex and challenging engineering problems Based on these facts, the motivation of exploring the capabilities of this optimizer is endeavoured and the authors work to improve the performance of the classical ELM model for the surface hydrology application. The motivation of exploring the capabilities of this optimizer is endeavoured and the authors work to improve the performance of the classical ELM model for the surface hydrology application For this reason, a real case of semiarid environment of the Tigris river in Iraq is investigated which is an extension for the published work [33].

Preliminaries

Case Study and Site Description

Application Results and Analysis

Conclusions