Development of hybrid wavelet-ANN model for hourly flood stage forecasting

Alexander, Ashlin Ann; Thampi, Santosh G.; Chithra, N. R.

doi:10.1080/09715010.2017.1422192

Cited by 20 publications

(13 citation statements)

References 17 publications

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“…The HBV model is a lumped conceptual hydrological model simulating the streamflow by partitioning the physical process into smaller components and modeling them using empirical equations. The model simulates the streamflow through the estimation of 10 model parameters in the four modules: (a) snowmelt and snow accumulation module, (b) soil moisture and effective precipitation module, (c) evapotranspiration module, and (d) runoff response module (Abebe et al., 2010; Aghakouchak & Habib, 2010; Aghakouchak et al., 2013; Bergström & Lindström, 2015; Engeland et al., 2010; Ouatiki et al., 2020; Seibert, 1997). The snowmelt and snow accumulation module estimate the snowmelt using the model parameter “Degree Day Factor" (DD).…”

Section: Methodsmentioning

confidence: 99%

A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy

Roy

Kasiviswanathan

Patidar

et al. 2023

Water Resources Research

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Section: Methodsmentioning

confidence: 99%

A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy

Roy

Kasiviswanathan

Patidar

et al. 2023

Water Resources Research

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“…In order to accomplish these tasks, a variety of techniques and approaches can be applied, such as rule-based systems (RBS), genetic algorithms, cellular automata, Fuzzy Systems, Multiagent systems, Swarm Intelligence, Case-based reasoning (CBR), and Artificial Neural Networks (ANN) (Chen et al, 2008). For example, AI (particularly genetic algorithms, Artificial Neural Networks, and Deep Learning) has been applied in a variety of civil engineering contexts including optimum design of structures (Hajela and Berke, 1991;Adeli and Park, 1995;Camp et al, 2003;Hadi, 2003), concrete strength modeling (Yeh, 1999;Ni and Wang, 2000;Lee and Ahn, 2003;Al-Salloum et al, 2012), predicting geotechnical settlement and liquefaction (Shahin et al, 2002;Young-Su and Byung-Tak, 2006), earthquake engineering (Lee and Han, 2002;Arslan, 2010;Yilmaz, 2011), concrete design mix (Jayaram et al, 2009), prediction and forecasting of water resources and flooding (Maier and Dandy, 2000;Mitra et al, 2016;Alexander et al, 2018;Lin et al, 2018;Yu et al, 2018;Zamanisabzi et al, 2018;Li et al, 2019), water quality and sediment modeling (Nagy et al, 2002;Zhang et al, 2010;Barzegar et al, 2016;Sabouri et al, 2016), irrigation and water-delivery scheduling (Nixon et al, 2001;Karasekreter et al, 2013), rainfallrunoff modeling (Minns and Hall, 1996;Tokar and Johnson, 1999;Cheng et al, 2005Cheng et al, , 2017Dixon, 2005;Jeong and Kim, 2005;…”

Section: Ai and Infrastructure Leadership In The Context Of Complexitymentioning

confidence: 99%

Opportunities and Challenges for Artificial Intelligence Applications in Infrastructure Management During the Anthropocene

2021

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Pervasive and accelerating climatic, technological, social, economic, and institutional change dictate that the challenges of the future will likely be vastly different and more complex than they are today. As our infrastructure systems (and their surrounding environment) become increasingly complex and beyond the cognitive understanding of any group of individuals or institutions, artificial intelligence (AI) may offer critical cognitive insights to ensure that systems adapt, services continue to be provided, and needs continue to be met. This paper conceptually links AI to various tasks and leadership capabilities in order to critically examine potential roles that AI can play in the management and implementation of infrastructure systems under growing complexity and uncertainty. Ultimately, various AI techniques appear to be increasingly well-suited to make sense of and operate under both stable (predictable) and chaotic (unpredictable) conditions. The ability to dynamically and continuously shift between stable and chaotic conditions is critical for effectively navigating our complex world. Thus, moving forward, a key adaptation for engineers will be to place increasing emphasis on creating the structural, financial, and knowledge conditions for enabling this type of flexibility in our integrated human-AI-infrastructure systems. Ultimately, as AI systems continue to evolve and become further embedded in our infrastructure systems, we may be implicitly or explicitly releasing control to algorithms. The potential benefits of this arrangement may outweigh the drawbacks. However, it is important to have open and candid discussions about the potential implications of this shift and whether or not those implications are desirable.

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“…ANN model provides large flexibility in solving non-linear problems, and it has been successfully applied in various hydrological areas [11] [12]. ANN has been used for flood forecasting due to its ability and efficiency in terms of computing time.…”

Section: Introductionmentioning

confidence: 99%

Flood Disaster and Early Warning: Application of ANFIS for River Water Level Forecasting

Faruq

Marto

Izzaty³

et al. 2021

KINETIK

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Intensively monitoring river water level and flows in both upstream and downstream catchments are essential for flood forecasting in disaster risk reduction. This paper presents a developed flood river water level forecasting utilizing a hybrid technique called adaptive neuro-fuzzy inference system (ANFIS) model, employed for Kelantan river basin, Kelantan state, Malaysia. The ANFIS model is designed to forecast river water levels at the downstream area in hourly lead times. River water level, rainfall, and river flows were considered as input variables located in upstream stations, and one river water level in the downstream station is chosen as flood forecasting point (FFP) target. Particularly, each of these input-output configurations consists of four stations located in different areas. About twenty-seven data with fifteen minutes basis recorded in January 2013 to March 2015 were used in training and testing the ANFIS network. Data preprocessing is done with feature reduction by principal component analysis and normalization as well. With more attributes in input configurations, the ANFIS model shows better result in term of coefficient correlation ( ) against artificial neural network (ANN)-based models and support vector machine (SVM) model. In general, it is proven that the presented ANFIS model is a capable machine learning approach for accurate forecasting of river water levels to predict floods for disaster risk reduction and early warning.

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Development of hybrid wavelet-ANN model for hourly flood stage forecasting

Cited by 20 publications

References 17 publications

A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy

A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy

Opportunities and Challenges for Artificial Intelligence Applications in Infrastructure Management During the Anthropocene

Flood Disaster and Early Warning: Application of ANFIS for River Water Level Forecasting

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