Hydrologic modeling plays a very important role in assessing the seasonal water availability, which is necessary to take decisions in water resources management. Both climate and land use and land cover change have great influence on the hydrological response of a watershed. The main aim of this study is to provide a detailed review of studies which have been carried out to analyze the hydrological impacts of land use change, and also to evaluate different scenario modeling approaches. In addition, a brief description of basic hydrologic models which are used to simulate the stream flow is provided. This review paper tried to explain the importance of model comparison and performance evaluation in modeling studies. The following conclusions may be drawn from this review: (1) it is necessary to model the possible impacts of land use change and climate change in order to proceed with effective water resources management, (2) it is important to analyze the variation in hydrological response in catchm...

https://www.tandfonline.com/doi/pdf/10.1080/23312041.2015.1115691

Impact of land use change on hydrological systems: A review of current modeling approaches

For predicting catchment runoff with data-driven methods, a long historical database of measurements is required. The current study focuses on the assessment of a deep learning model named long short-term memory (LSTM) for rainfall–runoff relationship with different training data size. The developed model has been evaluated on twenty catchments with diverse hydrological conditions obtained from the freely available CAMELS dataset. In order to prove the efficiency of the proposed model for runoff prediction, we test its performances against the traditional feed-forward neural network model. The studied models have been trained with the same input parameters and different size of training data to show the effect of data length on the prediction performances. To this end, the length of training data was varied from 3 to 15 years, while the model was tested on 10 years of data. The results show that the deep LSTM outperforms the traditional model in terms of statistical indicators over different size of training sets. The proposed deep LSTM model can predict runoff with acceptable performances using 9 years of data length in the training procedure, a result that improves when using 12 years. In addition, it has been proven that the deep LSTM model may be efficient even when using small data size (3 years) compared to its benchmarked model which require 9 years for similar results. Thus, the LSTM network is a powerful deep learning model able to learn the behavior of rainfall–runoff relationship with a minimum data length.

Impact of training data size on the LSTM performances for rainfall–runoff modeling

Soft Computing Approach for Rainfall-runoff Modelling: A Review

River flow (Qflow) is a hydrological process that considerably impacts the management and sustainability of water resources. The literature has shown great potential for nature-inspired optimized algorithms (NIOAs), like hybrid artificial intelligence (HAI) models, for Qflow modeling. Qflow forecasting needs to be accurate, robust, reliable, and capable of resolving complex non-linear problems to support the decision authority in local and national governments and NGOs. This extensive survey provides a literature review of 100-plus high-impact factor journal articles on developing NIOAs models during 2000–2022. This encompasses a comprehensive review of the established research in different climatic zones, NIOA types, artificial intelligence (AI) models, the input parameters used for model development, Qflow on different time scales, and model evaluation using a wide range of performance metrics. The review also assessed and evaluated several components of relevant literature, along with detailing the existing research gaps. Moreover, the global research gap with future direction is discussed based on current research limitations and possibilities. The data availability evaluation and futuristic suggestions are drafted logically. The review revealed the superiority of the NIOAs among all applied algorithms in the literature. Further, the review concludes that there is a need to improve technical aspects of Qflow forecasting and bridge the gap between scientific research, hydrometeorological model development, and real-world flood and drought management using probabilistic nature inspired (NI) forecasts, especially through effective communication. 

Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions

Sediment in river is usually transported during extreme events related to intense rainfall and high river flows. The conventional means of collecting data in such events are risky and costly compared to water discharge measurements. Hence, the lack of sediment data has prompted the use of sediment rating curves (SRC). The aim of this study is to explore the abilities of artificial neural networks (ANNs) in advancing the precision of stream flow-suspended discharge relationships during storm events in the Shiwen River, located in southern Taiwan. The ANNs used were multilayer perceptrons (MLP), the coactive neurofuzzy inference system model (CANFISM), time lagged recurrent networks (TLRN), fully recurrent neural networks (FRNN) and the radial basis function (RBF). A comparison is made between SRC and the ANNs. Hourly based water and sediment discharge during 8 storms were manually collected and used as inputs for the SRC and the ANNs. Results have shown that the ANN models were superior in reproducing hourly sediment discharge compared to SRC. The findings further suggest that MLP can provide the most accurate estimates of sediment discharge, (R2 of 0.903) compared to CANFISM, TLRN, FRNN and RBF. SRC had the lowest R2 (0.765), and resulted in underestimations of peak sediment discharge (−47%).

Estimating Sediment Discharge Using Sediment Rating Curves and Artificial Neural Networks in the Shiwen River, Taiwan

In Taiwan, owing to the nonuniform temporal and spatial distribution of rainfall and high mountains all over the country, hydrologic systems are very complex. Therefore, preventing and controlling flood disasters is imperative. Nevertheless, water level and flow records are essential in hydrological analysis for designing related water works of flood management. Due to the complexity of the hydrological process, reliable runoff is hardly predicted by applying linear and non-linear regression methods. Therefore, in this study, a model for estimating runoff by using rainfall data from a river basin is developed and a neural network technique is employed to recover missing data. For achieving the objectives, hourly rainfall and flow data from Nanhe, Taiwu, and Laii rainfall stations and Sinpi flow station in the Linbien basin are used. The data records were of 27 typhoons between the years 2005 and 2009. The feed forward back propagation network (FFBP) and conventional regression analysis (CRA) were employed to study their performances. From the statistical evaluation, it has been found that the performance of FFBP exceeded that of regression analysis as reflected by the determination coefficients R
 2, which were 0.969 and 0.284 for FFBP and CRA, respectively.

/pdf/using-artificial-neural-network-approach-for-modelling-35e0as4vrb.pdf

Using artificial neural network approach for modelling rainfall–runoff due to typhoon

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

I Tsou

Author Tools

Chat about Author

Papers

Using artificial neural network approach for modelling rainfall–runoff due to typhoon