A review on the applications of machine learning for runoff modeling

Mohammadi, Babak

doi:10.1007/s40899-021-00584-y

Cited by 68 publications

(24 citation statements)

References 73 publications

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“…Moreover, the easiest way to implement the model, associated to the low computation cost, as well as the fast training, validation, testing and evaluation with high performance compared to the physical models make it more accessible to the users. Several authors mentioned the accuracy of the machine learning models in comparison to the physical models (Mohammadi, 2021). Also according to Agudelo-Otalora et al (2018); Kratzert et al (2019) data driven models (such as machine learning) have proven to be even better than the physical, conceptual models in flood forecasting studies.…”

Section: Data Driven Model/machine Learningmentioning

confidence: 99%

Flood Forecasting and Warning System: A Survey of Models and Their Applications in West Africa

Fofana¹,

Adounkpè²,

Dotse³

et al. 2023

AJCC

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Flood events occurrences and frequencies in the world are of immense worry for the stability of the economy and life safety. Africa continent is the third continent the most negatively affected by the flood events after Asia and Europe. Eastern Africa is the most hit in Africa. However, Africa continent is at the early stage in term of flood forecasting models development and implementation. Very few hydrological models for flood forecasting are available and implemented in Africa for the flood mitigation. And for the majority of the cases, they need to be improved because of the time evolution. Flash flood in Bamako (Mali) has been putting both human life and the economy in jeopardy. Studying this phenomenon, as to propose applicable solutions for its alleviation in Bamako is a great concern. Therefore, it is of upmost importance to know the existing scientific works related to this situation in Mali and elsewhere. The main aim was to point out the various solutions implemented by various local and international institutions, in order to fight against the flood events. Two types of methods are used for the flood events adaptation: the structural and non-structural methods. The structural methods are essentially based on the implementation of the structures like the dams, dykes, levees, etc. The problem of these methods is that they may reduce the volume of water that will inundate the area but are not efficient for the prediction of the coming floods and cannot alert the population with any lead time in advance. The non-structural methods are the one allowing to perform the prediction with acceptable lead time. They used the hydrological rainfall-runoff How to cite this paper:

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Section: Data Driven Model/machine Learningmentioning

confidence: 99%

Flood Forecasting and Warning System: A Survey of Models and Their Applications in West Africa

Fofana¹,

Adounkpè²,

Dotse³

et al. 2023

AJCC

View full text Add to dashboard Cite

show abstract

“…[ [42][43][44] SVR Its increased generalisation ability, unique and globally optimum structures, and ability to be quickly trained. And SVR's flexibility is one of its strongest features, dependent on several types of kernel functions such as linear, polynomial, and radial basis function (RBF) kernels.…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

Khudhair

Zubaidi²,

Ortega‐Martorell³

et al. 2022

Environments

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Water quality has a significant influence on human health. As a result, water quality parameter modelling is one of the most challenging problems in the water sector. Therefore, the major factor in choosing an appropriate prediction model is accuracy. This research aims to analyse hybrid techniques and pre-processing data methods in freshwater quality modelling and forecasting. Hybrid approaches have generally been seen as a potential way of improving the accuracy of water quality modelling and forecasting compared with individual models. Consequently, recent studies have focused on using hybrid models to enhance forecasting accuracy. The modelling of dissolved oxygen is receiving more attention. From a review of relevant articles, it is clear that hybrid techniques are viable and precise methods for water quality prediction. Additionally, this paper presents future research directions to help researchers predict freshwater quality variables.

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“…Roy and Singh [11] developed a novel hybrid metaheuristic method for simulating the rainfall-runoff process that integrates Biogeography-Based Optimization (BBO), Particle Swarm Optimization (PSO), and grey wolf optimizer (GWO) combining ANN and Adaptive Network-based Fuzzy Inference Systems (ANFIS). Moreover, three optimization algorithms integrated with ANFIS were introduced for rainfall-runoff predictions, namely, Differential Evolution algorithm based ANFIS (ANFIS-DE), Particle Swarm Optimization based ANFIS (ANFIS-PSO), and Genetic Algorithm based ANFIS (ANFIS-GA) [53]. Investigating and contrasting these models in hydrology is strongly advised because the different algorithms have various advantages and VOLUME 11, 2023 distinct methods for complex modelling phenomena.…”

Section: Introductionmentioning

confidence: 99%

Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka

Rathnayake

Chathuranika

et al. 2023

IEEE Access

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The Rainfall-Runoff (R-R) relationship is essential to the hydrological cycle. Sophisticated hydrological models can accurately investigate R-R relationships; however, they require many data. Therefore, machine learning and soft computing techniques have taken the attention in the environment of limited hydrological, meteorological, and geological data. The accuracy of such models depends on the various parameters, including the quality of inputs and outputs and the used algorithms. However, identifying a perfect algorithm is still challenging. This study develops a fuzzy logic-based algorithm called Cascaded-ANFIS to accurately predict runoff based on rainfall. The model was compared against three regression algorithms: Long Short-Term Memory, Grated Recurrent Unit, and Recurrent Neural Networks. These algorithms have been selected due to their outstanding performances in similar studies. The models were tested on the Mahaweli River, the longest in Sri Lanka. The results showcase that the Cascaded-ANFISbased model outperforms the other algorithms. The correlation coefficient of each algorithm's predictions was 0.9330, 0.9120, 0.9133, 0.8915, 0.6811, 0.6811, and 0.6734 for the Cascaded-ANFIS, LSTM, GRU, RNN, Linear, Ridge, and Lasso regression models respectively. Hence, this study concludes that the proposed algorithm is 21% more accurate than the second-best LSTM algorithm. In addition, Shared Socio-economic Pathways (SSP2-4.5 and SSP5-8.5 scenarios) were used to generate future rainfalls, forecast the near-future and mid-future water levels, and identify potential flood events. The future forecasting results indicate a decrease in flood events and magnitudes in both SSP2-4.5 and SSP5-8.5 scenarios. Furthermore, the SSP5-8.5 scenario shows drought weather from May to August yearly. The results of this study can effectively be used to manage and control water resources and mitigate flood damages.

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A review on the applications of machine learning for runoff modeling

Cited by 68 publications

References 73 publications

Flood Forecasting and Warning System: A Survey of Models and Their Applications in West Africa

Flood Forecasting and Warning System: A Survey of Models and Their Applications in West Africa

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka

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