| 1. |
- Achite, Mohammed, et al.
(författare)
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An improved adaptive neuro-fuzzy inference system for hydrological drought prediction in Algeria
- 2023
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Ingår i: Physics and Chemistry of the Earth. - 1474-7065. ; 131
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Tidskriftsartikel (refereegranskat)abstract
- Drought has negative impacts on water resources, food security, soil degradation, desertification and agricultural productivity. The meteorological and hydrological droughts prediction using standardized precipitation/runoff indices (SPI/SRI) is crucial for effective water resource management. In this study, we suggest ANFISWCA, an adaptive neuro-fuzzy inference system (ANFIS) optimized by the water cycle algorithm (WCA), for hydrological drought forecasting in semi-arid regions of Algeria. The new model was used to predict SRI at 3-, 6-, 9-, and 12-month accumulation periods in the Wadi Mina basin, Algeria. The results of the model were assessed using four criteria; determination coefficient, mean absolute error, variance accounted for, and root mean square error, and compared with those of the standalone ANFIS model. The findings suggested that throughout the testing phase at all the sub-basins, the proposed hybrid model outperformed the conventional model for estimating drought. This study indicated that the WCA algorithm enhanced the ANFIS model's drought forecasting accuracy. The proposed model could be employed for forecasting drought at multi-timescales, deciding on remedial strategies for dealing with drought at study stations, and aiding in sustainable water resources management.
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| 2. |
- Achite, Mohammed, et al.
(författare)
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Application of multiple spatial interpolation approaches to annual rainfall data in the Wadi Cheliff basin (north Algeria)
- 2024
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Ingår i: Ain Shams Engineering Journal. - 2090-4479. ; 15:3
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Tidskriftsartikel (refereegranskat)abstract
- This study addresses a challenging problem of predicting mean annual precipitation across arid and semi-arid areas in northern Algeria, utilizing deterministic, geostatistical (GS), and machine learning (ML) models. Through the analysis of data spanning nearly five decades and encompassing 150 monitoring stations, the result of Random Forest showed the highest training performance, with R square value (of 0.9524) and the Root Mean Square Error (of 24.98). Elevation emerges as a critical factor, enhancing prediction accuracy in mountainous and complex terrains when used as an auxiliary variable. Cluster analysis further refines our understanding of station distribution and precipitation characteristics, identifying four distinct clusters, each exhibiting unique precipitation patterns and elevation zones. This study helps for a better understanding of precipitation prediction, encouraging the integration of additional variables and the exploration of climate change impacts, thereby contributing to informed environmental management and adaptation strategies across diverse climatic and terrain scenarios.
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| 3. |
- Achite, Mohammed, et al.
(författare)
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Enhancing Rainfall-Runoff Simulation via Meteorological Variables and a Deep-Conceptual Learning-Based Framework
- 2022
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Ingår i: Atmosphere. - : MDPI AG. - 2073-4433. ; 13:10
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Tidskriftsartikel (refereegranskat)abstract
- Accurate streamflow simulation is crucial for many applications, such as optimal reservoir operation and irrigation. Conceptual techniques employ physical ideas and are suitable for representing the physics of the hydrologic model, but they might fail in competition with their more advanced counterparts. In contrast, deep learning (DL) approaches provide a great computational capability for streamflow simulation, but they rely on data characteristics and the physics of the issue cannot be fully understood. To overcome these limitations, the current study provided a novel framework based on a combination of conceptual and DL techniques for enhancing the accuracy of streamflow simulation in a snow-covered basin. In this regard, the current study simulated daily streamflow in the Kalixälven river basin in northern Sweden by integrating a snow-based conceptual hydrological model (MISD) with a DL model. Daily precipitation, air temperature (average, minimum, and maximum), dew point temperature, evapotranspiration, relative humidity, sunshine duration, global solar radiation, and atmospheric pressure data were used as inputs for the DL model to examine the effect of each meteorological variable on the streamflow simulation. Results proved that adding meteorological variables to the conceptual hydrological model underframe of parallel settings can improve the accuracy of streamflow simulating by the DL model. The MISD model simulated streamflow had an MAE = 8.33 (cms), r = 0.88, and NSE = 0.77 for the validation phase. The proposed deep-conceptual learning-based framework also performed better than the standalone MISD model; the DL method had an MAE = 7.89 (cms), r = 0.90, and NSE = 0.80 for the validation phase when meteorological variables and MISD results were combined as inputs for the DL model. The integrated rainfall-runoff model proposed in this research is a new concept in rainfall-runoff modeling which can be used for accurate streamflow simulations.
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| 4. |
- Anaraki, Mahdi Valikhan, et al.
(författare)
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Modeling of Monthly Rainfall–Runoff Using Various Machine Learning Techniques in Wadi Ouahrane Basin, Algeria
- 2023
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Ingår i: Water. - : MDPI. - 2073-4441. ; 15:20
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Tidskriftsartikel (refereegranskat)abstract
- Rainfall–runoff modeling has been the core of hydrological research studies for decades. To comprehend this phenomenon, many machine learning algorithms have been widely used. Nevertheless, a thorough comparison of machine learning algorithms and the effect of pre-processing on their performance is still lacking in the literature. Therefore, the major objective of this research is to simulate rainfall runoff using nine standalone and hybrid machine learning models. The conventional models include artificial neural networks, least squares support vector machines (LSSVMs), K-nearest neighbor (KNN), M5 model trees, random forests, multiple adaptive regression splines, and multivariate nonlinear regression. In contrast, the hybrid models comprise LSSVM and KNN coupled with a gorilla troop optimizer (GTO). Moreover, the present study introduces a new combination of the feature selection method, principal component analysis (PCA), and empirical mode decomposition (EMD). Mean absolute error (MAE), root mean squared error (RMSE), relative RMSE (RRMSE), person correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), and Kling Gupta efficiency (KGE) metrics are used for assessing the performance of the developed models. The proposed models are applied to rainfall and runoff data collected in the Wadi Ouahrane basin, Algeria. According to the results, the KNN–GTO model exhibits the best performance (MAE = 0.1640, RMSE = 0.4741, RRMSE = 0.2979, R = 0.9607, NSE = 0.9088, and KGE = 0.7141). These statistical criteria outperform other developed models by 80%, 70%, 72%, 77%, 112%, and 136%, respectively. The LSSVM model provides the worst results without pre-processing the data. Moreover, the findings indicate that using feature selection, PCA, and EMD significantly improves the accuracy of rainfall–runoff modeling.
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| 5. |
- Emami, Somayeh, et al.
(författare)
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Application of ANFIS, ELM, and ANN models to assess water productivity indicators based on agronomic techniques in the Lake Urmia Basin
- 2023
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Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 13:2
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Tidskriftsartikel (refereegranskat)abstract
- Water productivity (WP) is one of the most important critical indicators in the essential planning of water consumption in the agricultural sector. For this purpose, the WP and economic water productivity (WPe) were estimated using agronomic technologies. The impact of agronomic technologies on WP and WPe was carried out in two parts of field monitoring and modeling using novel intelligent approaches. Extreme learning machine (ELM), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN) methods were used to model WP and WPe. A dataset including 200 field data was collected from five treatment and control sections in the Malekan region, located in the southeast of Lake Urmia, Iran, for the crop year 2020–2021. Six different input combinations were introduced to estimate WP and WPe. The models used were evaluated using mean squared error (RMSE), relative mean squared error (RRMSE), and efficiency measures (NSE). Field monitoring results showed that in the treatment fields, with the application of agronomic technologies, the crop yield, WP, and WPe increased by 17.9%, 30.1%, and 19.9%, respectively. The results explained that irrigation water in farms W1, W2, W3, W4, and W5 decreased by 23.9%, 21.3%, 29.5%, 16.5%, and 2.7%, respectively. The modeling results indicated that the ANFIS model with values of RMSE = 0.016, RRMSE = 0.018, and NSE = 0.960 performed better in estimating WP and WPe than ANN and ELM models. The results confirmed that the crop variety, fertilizer, and irrigation plot dimensions are the most critical influencing parameters in improving WP and WPe.
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| 6. |
- Hai, Tao, et al.
(författare)
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Global Solar Radiation Estimation and Climatic Variability Analysis Using Extreme Learning Machine Based Predictive Model
- 2020
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Ingår i: IEEE Access. - USA : IEEE. - 2169-3536. ; 8, s. 12026-12042
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Tidskriftsartikel (refereegranskat)abstract
- Sustainable utilization of the freely available solar radiation as renewable energy source requires accurate predictive models to quantitatively evaluate future energy potentials. In this research, an evaluation of the preciseness of extreme learning machine (ELM) model as a fast and efficient framework for estimating global incident solar radiation (G) is undertaken. Daily meteorological datasets suitable for G estimation belongs to the northern parts of the Cheliff Basin in Northwest Algeria, is used to construct the estimation model. Cross-correlation functions are applied between the inputs and the target variable (i.e., G) where several climatological information’s are used as the predictors for surface level G estimation. The most significant model inputs are determined in accordance with highest cross-correlations considering the covariance of the predictors with the G dataset. Subsequently, seven ELM models with unique neuronal architectures in terms of their input-hidden-output neurons are developed with appropriate input combinations. The prescribed ELM model’s estimation performance over the testing phase is evaluated against multiple linear regressions (MLR), autoregressive integrated moving average (ARIMA) models and several well-established literature studies. This is done in accordance with several statistical score metrics. In quantitative terms, the root mean square error (RMSE) and mean absolute error (MAE) are dramatically lower for the optimal ELM model with RMSE and MAE = 3.28 and 2.32 Wm −2 compared to 4.24 and 3.24 Wm −2 (MLR) and 8.33 and 5.37 Wm −2 (ARIMA).
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| 7. |
- Saroughi, Mohsen, et al.
(författare)
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Evaluate effect of 126 pre-processing methods on various artificial intelligence models accuracy versus normal mode to predict groundwater level (case study: Hamedan-Bahar Plain, Iran)
- 2024
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Ingår i: Heliyon. - : Elsevier Ltd. - 2405-8440. ; 10:7
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Tidskriftsartikel (refereegranskat)abstract
- The estimation of groundwater levels is crucial and an important step in ensuring sustainable management of water resources. In this paper, selected piezometers of the Hamedan-Bahar plain located in west of Iran. The main objective of this study is to compare effect of various pre-processing methods on input data for different artificial intelligence (AI) models to predict groundwater levels (GWLs). The observed GWL, evaporation, precipitation, and temperature were used as input variables in the AI algorithms. Firstly, 126 method of data pre-processing was done by python programming which are classified into three classes: 1- statistical methods, 2- wavelet transform methods and 3- decomposition methods; later, various pre-processed data used by four types of widely used AI models with different kernels, which includes: Support Vector Machine (SVR), Artificial Neural Network (ANN), Long-Short Term memory (LSTM), and Pelican Optimization Algorithm (POA) - Artificial Neural Network (POA-ANN) are classified into three classes: 1- machine learning (SVR and ANN), 2- deep learning (LSTM) and 3- hybrid-ML (POA-ANN) models, to predict groundwater levels (GWLs). Akaike Information Criterion (AIC) were used to evaluate and validate the predictive accuracy of algorithms. According to the results, based on summation (train and test phases) of AIC value of 1778 models, average of AIC values for ML, DL, hybrid-ML classes, was decreased to −25.3%, −29.6% and −57.8%, respectively. Therefore, the results showed that all data pre-processing methods do not lead to improvement of prediction accuracy, and they should be selected very carefully by trial and error. In conclusion, wavelet-ANN model with daubechies 13 and 25 neurons (db13_ANN_25) is the best model to predict GWL that has −204.9 value for AIC which has grown by 5.23% (−194.7) compared to the state without any pre-processing method (ANN_Relu_25).
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