Using the MODIS Sensor for Snow Cover Modeling and the Assessment of Drought Effects on Snow Cover in a Mountainous Area

Aghelpour, Pouya; Guan, Yiqing; Bahrami-Pichaghchi, Hadigheh; Mohammadi, Babak; Kisi, Özgür; Zhang, Danrong

doi:10.3390/rs12203437

Cited by 30 publications

(10 citation statements)

References 35 publications

(52 reference statements)

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“…The applied models led to desirable predictions in arid, semi-arid, and Mediterranean climates, while humid climate provided the weakest predictions. Aghelpour et al (2020c) investigated the snow cover of the Northern and Southern slopes of Alborz Mountains in Iran by considering two issues: (1) estimating the snow cover area and (2) investigating the effects of droughts on snow cover. The results showed that the effects of a drought event on the snow cover area would remain up to 5 (or 6) months in the region.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Hybrid point and interval prediction approaches for drought modeling using ground-based and remote sensing data

Roushangar

Ghasempour

Kirca

et al. 2024

Artificial Intelligence in Hydrology

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Drought as a severe natural disaster has devastating effects on the environment; therefore, reliable drought prediction is an important issue. In the current study, based on lower upper bound estimation, hybrid models including data preprocessing, permutation entropy, and artificial intelligence (AI) methods were used for point and interval predictions of short- to long-term series of Standardized Precipitation Index in the Northwest of Iran. Ground-based and remote sensing precipitation data were used covering the period of 1983–2017. In the modeling process, first, the data processing capability via variational mode decomposition (VMD), ensemble empirical mode decomposition, and permutation entropy (PE) was investigated in drought point prediction. Then, interval prediction was applied for tolerating increased uncertainty and providing more details for practical operation decisions. The simulation results demonstrated that the proposed integrated models could achieve significantly better performance compared to single models. Hybrid PE models increased the modeling accuracy up to 40 and 55%. Finally, the efficiency of developed models was verified for Normalized Difference Vegetation Index prediction. Results demonstrated that the proposed methodology based on remote sensing data and VMD–PE–AI approaches could be successfully used for drought modeling, especially in limited or non-gauged areas.

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Section: Graphical Abstract Introductionmentioning

confidence: 99%

Hybrid point and interval prediction approaches for drought modeling using ground-based and remote sensing data

Roushangar

Ghasempour

Kirca

et al. 2024

Artificial Intelligence in Hydrology

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“…Forecasting the evapotranspiration rates, through providing information on the future status of evapotranspiration at different time scales can be of great help in making appropriate decisions, planning as well as applying management methods of water resources. Data-driven models such as stochastic and artificial intelligence methods are efficient approaches that have shown good performance in modeling and predicting hydrometeorological variables in recent years (Aghelpour et al, 2021c;Mohammadi et al, 2020;Aghelpour et al 2020b). Karbasi (2018) used AIs in forecasting ET0 for 1, 2, 3, 7, 10, 14, 18, 24, and 30 days' horizons.…”

Section: Introductionmentioning

confidence: 99%

Comparing The Models SARIMA, ANFIS And ANFIS-DE In Forecasting Monthly Evapotranspiration Rates Under Heterogeneous Climatic Conditions

Aghelpour

Varshavian

Hamedi

2021

Preprint

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Reference crop evapotranspiration (ET0) is one of the most important hydro-climatological components which directly affects agricultural productions, and its forecasting is critical for water managers and irrigation planners. In this study, adaptive neuro-fuzzy inference system (ANFIS) model has been hybridized by differential evolution (DE) optimization algorithm as a novel approach to forecast monthly ET0. Furthermore, this model has been compared with the classic stochastic time series model. For this, the ET0 rates were calculated on monthly scale during 1995-2018, based on FAO-56 Penman-Monteith equation and meteorological data including: minimum air temperature, maximum air temperature, mean air temperature, minimum relative humidity, maximum relative humidity & sunshine duration. The investigation was performed on 6 stations in different climates of Iran, including: Bandar Anzali & Ramsar (per-humid), Gharakhil (sub-humid), Shiraz (semi-arid), Ahwaz (arid) and Yazd (extra-arid). The models’ performances were evaluated by the criteria percent bias (PB), root mean squared error (RMSE), normalized RMSE (NRMSE) and Nash-Sutcliff (NS) coefficient. Surveys confirm the high capability of the hybrid ANFIS-DE model in monthly ET0 forecasting; so that the DE algorithm was able to improve the accuracy of ANFIS, by 16% on average. Seasonal autoregressive integrated moving average (SARIMA) was the most suitable pattern among the time series stochastic models, and superior compared to its other competitors. Consequently, due to the simplicity and parsimony, the SARIMA was suggested more appropriate for monthly ET0 forecasting in all the climates. Comparison between the different climates confirmed that the climate type significantly affects the forecasting accuracies: it’s revealed that all the models work better in extra-arid, arid and semi-arid climates, than the humid and per-humid areas.

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“…This type of neural network is one type of multipurpose basic and approximating radial neural networks for smooth functions. GRNN is a three-layer neural network in which, like the other neural networks [48]. The number of neurons existent on the first and last layers is, respectively, equal to the dimensions of the input and output vectors, but, unlike the other networks, the number of the neurons in the hidden layer of GRNN is equal to the number of the observed data.…”

Section: Generalized Regression Neural Network (Grnn)mentioning

confidence: 99%

“…This type of neural network uses normal efficiency function in each of the neurons of the hidden layer, and the data inputted into this function for each neuron include the Euclidean distance between the input and observed data related to that neuron. See the sources for complementary information on GRNN [45,48,49].…”

Section: Generalized Regression Neural Network (Grnn)mentioning

confidence: 99%

A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods

Aghelpour

Mohammadi

Biazar

et al. 2020

IJGI

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Precipitation deficit can affect different natural resources such as water, soil, rivers and plants, and cause meteorological, hydrological and agricultural droughts. Multivariate drought indexes can theoretically show the severity and weakness of various drought types simultaneously. This study introduces an approach for forecasting joint deficit index (JDI) and multivariate standardized precipitation index (MSPI) by using machine–learning methods and entropy theory. JDI and MSPI were calculated for the 1–12 months’ time window (JDI1–12 and MSPI1–12), using monthly precipitation data. The methods implemented for forecasting are group method of data handling (GMDH), generalized regression neural network (GRNN), least squared support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS) and ANFIS optimized with three heuristic optimization algorithms, differential evolution (DE), genetic algorithm (GA) and particle swarm optimization (PSO) as meta-innovative methods (ANFIS-DE, ANFIS-GA and ANFIS-PSO). Monthly precipitation, monthly temperature and previous amounts of the index’s values were used as inputs to the models. Data from 10 synoptic stations situated in the widest climatic zone of Iran (extra arid-cold climate) were employed. Optimal model inputs were selected by gamma test and entropy theory. The evaluation results, which were given using mean absolute error (MAE), root mean squared error (RMSE) and Willmott index (WI), show that the machine learning and meta-innovative models can present acceptable forecasts of general drought’s conditions. The algorithms DE, GA and PSO, could improve the ANFIS’s performance by 39.4%, 38.7% and 22.6%, respectively. Among all the applied models, the GMDH shows the best forecasting accuracy with MAE = 0.280, RMSE = 0.374 and WI = 0.955. In addition, the models could forecast MSPI better than JDI in the majority of cases (stations). Among the two methods used to select the optimal inputs, it is difficult to select one as a better input selector, but according to the results, more attention can be paid to entropy theory in drought studies.

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Using the MODIS Sensor for Snow Cover Modeling and the Assessment of Drought Effects on Snow Cover in a Mountainous Area

Cited by 30 publications

References 35 publications

Hybrid point and interval prediction approaches for drought modeling using ground-based and remote sensing data

Hybrid point and interval prediction approaches for drought modeling using ground-based and remote sensing data

Comparing The Models SARIMA, ANFIS And ANFIS-DE In Forecasting Monthly Evapotranspiration Rates Under Heterogeneous Climatic Conditions

A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods

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