Modeling Hydrological Responses of Watershed Under Climate Change Scenarios Using Machine Learning Techniques

Karimizadeh, Keivan; Yi, Jaeeung

doi:10.1007/s11269-023-03603-z

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…Numerous studies have recognized that the ability of ANN methods to establish connections between output and input variables in complex hydrological phenomena, without the need for prior information about the nature of the phenomenon, has significantly boosted the adoption of these methods in hydrological modeling (Hsu, 1995;Govindaraju, 2000;Prakash et al, 2014;Taormina et al, 2015;Chen & Chau, 2019;Tamiru & Dinka, 2021;Gomes et al, 2021;Filipova et al, 2022;Karimizadeh, 2023).…”

Section: Introductionmentioning

confidence: 99%

Multi-Step-Ahead Rainfall-Runoff Modeling: Decision Tree-Based Clustering for Hybrid Wavelet Neural- Networks Modeling

Molajou,

Nourani,

Tajbakhsh

et al. 2024

Preprint

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This paper introduces a novel hybrid approach for predicting the rainfall-runoff (r-r) phenomenon across different data division scenarios (50%-50%, 60%-40%, and 75%-25%) within two distinct watersheds, encompassing both monthly and daily scales. Additionally, the effectiveness of this newly proposed hybrid method is evaluated in multi-step ahead prediction (MSAP) scenarios. The proposed method comprises three primary steps. Initially, to address the non-stationarity of the runoff and rainfall time series, these series are decomposed into multiple sub-time series using the wavelet (WT) decomposition method. Subsequently, in the second step, the decomposed sub-series are utilized as input data for the M5 model tree, a decision tree-based model. The M5 model tree classifies the samples of decomposed runoff and rainfall time series into distinct classes. Finally, each class is modeled using an artificial neural network (ANN). The results demonstrate the superior efficiency of the proposed WT-M5-ANN method compared to other available hybrid methods. Specifically, the calculated R2 was 0.93 for the proposed WT-M5-ANN method, whereas it was 0.89 and 0.81 for the WT-ANN and WT-M5 methods, respectively, for the Lobbs Hole Creek watershed at the daily scale.

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Section: Introductionmentioning

confidence: 99%

Multi-Step-Ahead Rainfall-Runoff Modeling: Decision Tree-Based Clustering for Hybrid Wavelet Neural- Networks Modeling

Molajou,

Nourani,

Tajbakhsh

et al. 2024

Preprint

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show abstract

“…Water serves as a critical elemental resource essential for both the sustenance and progress of humanity. However, with the impact of global climate change and human activities, the availability of water resources is steadily diminishing [1]. The efficient and rational utilization of water resources has, thus, emerged as a global concern.…”

Section: Introductionmentioning

confidence: 99%

Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method

Lin,

Wang,

Jiang

et al. 2024

Water

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Reliable streamflow forecasting is a determining factor for water resource planning and flood control. To better understand the strengths and weaknesses of newly proposed methods in streamflow forecasting and facilitate comparisons of different research results, we test a simple, universal, and efficient benchmark method, namely, the naïve method, for short-term streamflow prediction. Using the naïve method, we assess the streamflow forecasting performance of the long short-term memory models trained with different objective functions, including mean squared error (MSE), root mean squared error (RMSE), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), and mean absolute error (MAE). The experiments over 273 watersheds show that the naïve method attains good forecasting performance (NSE > 0.5) in 88%, 65%, and 52% of watersheds at lead times of 1 day, 2 days, and 3 days, respectively. Through benchmarking by the naïve method, we find that the LSTM models trained with squared-error-based objective functions, i.e., MSE, RMSE, NSE, and KGE, perform poorly in low flow forecasting. This is because they are more influenced by training samples with high flows than by those with low flows during the model training process. For comprehensive short-term streamflow modeling without special demand orientation, we recommend the application of MAE instead of a squared-error-based metric as the objective function. In addition, it is also feasible to perform logarithmic transformation on the streamflow data. This work underscores the critical importance of appropriately selecting the objective functions for model training/calibration, shedding light on how to effectively evaluate the performance of streamflow forecast models.

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Multi-Step-Ahead Rainfall-Runoff Modeling: Decision Tree-Based Clustering for Hybrid Wavelet Neural- Networks Modeling

Molajou,

Nourani,

Tajbakhsh

et al. 2024

Water Resour Manage

View full text Add to dashboard Cite

This paper introduces a novel hybrid approach for predicting the rainfall-runoff (r-r) phenomenon across different data division scenarios (50%-50%, 60%-40%, and 75%-25%) within two distinct watersheds, encompassing both monthly and daily scales. Additionally, the effectiveness of this newly proposed hybrid method is evaluated in multi-step ahead prediction (MSAP) scenarios. The proposed method comprises three primary steps. Initially, to address the non-stationarity of the runoff and rainfall time series, these series are decomposed into multiple sub-time series using the wavelet (WT) decomposition method. Subsequently, in the second step, the decomposed sub-series are utilized as input data for the M5 model tree, a decision tree-based model. The M5 model tree classifies the samples of decomposed runoff and rainfall time series into distinct classes. Finally, each class is modeled using an artificial neural network (ANN). The results demonstrate the superior efficiency of the proposed WT-M5-ANN method compared to other available hybrid methods. Specifically, the calculated R 2 was 0.93 for the proposed WT-M5-ANN method, whereas it was 0.89 and 0.81 for the WT-ANN and WT-M5 methods, respectively, for the Lobbs Hole Creek watershed at the daily scale.

show abstract

Modeling Hydrological Responses of Watershed Under Climate Change Scenarios Using Machine Learning Techniques

Cited by 6 publications

References 17 publications

Multi-Step-Ahead Rainfall-Runoff Modeling: Decision Tree-Based Clustering for Hybrid Wavelet Neural- Networks Modeling

Multi-Step-Ahead Rainfall-Runoff Modeling: Decision Tree-Based Clustering for Hybrid Wavelet Neural- Networks Modeling

Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method

Multi-Step-Ahead Rainfall-Runoff Modeling: Decision Tree-Based Clustering for Hybrid Wavelet Neural- Networks Modeling

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