Application of several data-driven techniques to predict a standardized precipitation index

Atmósfera ◽  
2016 ◽  
Vol 29 (2) ◽  
pp. 121 ◽  
Author(s):  
Bahram Choubin ◽  
Arash Malekian ◽  
Mohammad Gloshan
Keyword(s):  
Large Scale ◽  
Fuzzy Inference ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Climate Indices ◽  
Inference System ◽  
Model Tree ◽  
Climate Signals ◽  
M5p Model Tree ◽  
Semi Arid

Climate modeling and prediction is important in water resources management, especially in arid and semi-arid regions that frequently suffer further from water shortages. The Maharlu-Bakhtegan basin, with an area of 31 000 km2 is a semi-arid and arid region located in southwestern Iran. Therefore, precipitation and water shortage in this area have many problems. This study presents a drought index modeling approach based on large-scale climate indices by using the adaptive neuro-fuzzy inference system (ANFIS), the M5P model tree and the multilayer perceptron (MLP). First, most of the climate signals were determined from 25 climate signals using factor analysis, and subsequently, the standardized precipitation index (SPI) was predicted one to 12 months in advance with ANFIS, the M5P model tree and MLP. The evaluation of the models performance by error parameters and Taylor diagrams demonstrated that performance of the MLP is better than the other models. The results also revealed that the accuracy of prediction increased considerably by using climate indices of the previous month (t – 1) (RMSE = 0.802, ME = –0.002 and PBIAS = –0.47).

scholarly journals Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models

Climate ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 28
Author(s):  
Anurag Malik ◽  
Anil Kumar ◽  
Priya Rai ◽  
Alban Kuriqi
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Vital Role ◽  
Precipitation Index ◽  
Visual Interpretation ◽  
Inference System ◽  
Drought Prediction ◽  
Optimal Functioning

Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) and Co-Active Neuro-Fuzzy Inference System (CANFIS), and regression, model including Multiple Linear Regression (MLR), were investigated for multi-scalar Standardized Precipitation Index (SPI) prediction in the Garhwal region of Uttarakhand State, India. The SPI was computed on six different scales, i.e., 1-, 3-, 6-, 9-, 12-, and 24-month, by deploying monthly rainfall information of available years. The significant lags as inputs for the MLPNN, CANFIS, and MLR models were obtained by utilizing Partial Autocorrelation Function (PACF) with a significant level equal to 5% for SPI-1, SPI-3, SPI-6, SPI-9, SPI-12, and SPI-24. The predicted multi-scalar SPI values utilizing the MLPNN, CANFIS, and MLR models were compared with calculated SPI of multi-time scales through different performance evaluation indicators and visual interpretation. The appraisals of results indicated that CANFIS performance was more reliable for drought prediction at Dehradun (3-, 6-, 9-, and 12-month scales), Chamoli and Tehri Garhwal (1-, 3-, 6-, 9-, and 12-month scales), Haridwar and Pauri Garhwal (1-, 3-, 6-, and 9-month scales), Rudraprayag (1-, 3-, and 6-month scales), and Uttarkashi (3-month scale) stations. The MLPNN model was best at Dehradun (1- and 24- month scales), Tehri Garhwal and Chamoli (24-month scale), Haridwar (12- and 24-month scales), Pauri Garhwal (12-month scale), Rudraprayag (9-, 12-, and 24-month), and Uttarkashi (1- and 6-month scales) stations, while the MLR model was found to be optimal at Pauri Garhwal (24-month scale) and Uttarkashi (9-, 12-, and 24-month scales) stations. Furthermore, the modeling approach can foster a straightforward and trustworthy expert intelligent mechanism for projecting multi-scalar SPI and decision making for remedial arrangements to tackle meteorological drought at the stations under study.

scholarly journals Application of Markov chains to Standardized Precipitation Index (SPI) in São Francisco River Basin

2019 ◽  
Vol 14 (3) ◽  
pp. 1
Author(s):  
Esdras Adriano Barbosa dos Santos ◽  
Tatijana Stosic ◽  
Ikaro Daniel de Carvalho Barreto ◽  
Laélia Campos ◽  
Antonio Samuel Alves da Silva
Keyword(s):  
Markov Chains ◽  
River Basin ◽  
Arid Region ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Severe Drought ◽  
Sao Francisco River ◽  
São Francisco River ◽  
Semi Arid Region ◽  
Semi Arid

This work evaluated dry and rainy conditions in the subregions of the São Francisco River Basin (BHSF) using the Standardized Precipitation Index (SPI) and Markov chains. Each subregion of the BHSF has specific physical and climatic characteristics. The data was obtained from the National Water Agency (ANA), collected by four pluviometric stations (representative of each subregion), covering 46 years of data, from 1970 to 2015. The SPI was calculated for the time scales of six and twelve months and transition probabilities were obtained using the Markov chain. Transition matrices showed that, at both scales, if the climate conditions were severe drought or rainy, switching to another class would be unlikely in the short term.  Correlating this information with the probabilities of the stationary distribution, it was possible to find the regions that are most likely to be under rainy or dry weather in the future. The recurrence times calculated for the stations that belong to the semi-arid region were smaller when compared to the value of the return period of the representative station of Upper São Francisco that has higher levels of precipitation, confirming the predisposition of the semi-arid region to present greater chances of future periods of drought.

scholarly journals Multiperiod-Ahead Wind Speed Forecasting Using Deep Neural Architecture and Ensemble Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Lei Chen ◽  
Zhijun Li ◽  
Yi Zhang
Keyword(s):  
Wind Speed ◽  
Ensemble Learning ◽  
Analysis Of Variance ◽  
Large Scale ◽  
Fuzzy Inference ◽  
Feature Learning ◽  
Denoising Autoencoder ◽  
Inference System ◽  
Wind Speed Forecasting ◽  
Learning Machine

Accurate forecasting of wind speed plays a fundamental role in enabling reliable operation and planning for large-scale integration of wind turbines. It is difficult to obtain the accurate wind speed forecasting (WSF) due to the intermittent and random nature of wind energy. In this paper, a multiperiod-ahead WSF model based on the analysis of variance, stacked denoising autoencoder (SDAE), and ensemble learning is proposed. The analysis of variance classifies the training samples into different categories. The stacked denoising autoencoder as a deep learning architecture is later built for unsupervised feature learning in each category. The ensemble of extreme learning machine (ELM) is applied to fine-tune the SDAE for multiperiod-ahead wind speed forecasting. Experimental results are made to demonstrate that the proposed model has the best performance compared with the classic WSF methods including the single SDAE-ELM, ELMAN, and adaptive neuron-fuzzy inference system (ANFIS).

scholarly journals Spatial and Temporal Analysis of Drought in Nepal using Standardized Precipitation Index and its Relationship with Climate Indices

1970 ◽  
Vol 7 (1) ◽  
pp. 59-74 ◽  
Author(s):  
M Sigdel ◽  
M Ikeda
Keyword(s):  
Principal Component Analysis ◽  
Southern Oscillation ◽  
Climatic Variability ◽  
Standardized Precipitation Index ◽  
Principal Component ◽  
Component Analysis ◽  
Precipitation Index ◽  
Climate Indices ◽  
Indian Ocean Dipole Mode ◽  
Time Space

Drought over Nepal is studied on the basis of precipitation as a key parameter. Using monthly mean precipitation data for a period of 33 years, Standardized Precipitation Index (SPI) is produced for the drought analysis with the time scale of 3 months (SPI-3) and 12 months (SPI-12) as they are applicable for agriculture and hydrological aspects, respectively. Time-space variability is explored based on Principal Component Analysis (PCA) along with Rotated PCA (RPCA). Four rotated components were explored for both SPI-3 and SPI-12 representing climatic variability with cores over eastern, central and western Nepal separately. Droughts associated with SPI-3 occurred almost evenly over these regions. Droughts associated with SPI-12 were consistent with SPI-3 for summer, since summer precipitation dominates annual precipitation. Connection between SPI and the climate indices such as Southern Oscillation Index (SOI) and Indian Ocean Dipole Mode Index (DMI) was studied, suggesting that one of the causes for summer droughts is El Nino, while the winter droughts could be related with positive DMI. Keywords: Standardized Precipitation Index; Nepal; Principal component analysis; Drought DOI: http://dx.doi.org/10.3126/jhm.v7i1.5617 JHM 2010; 7(1): 59-74

scholarly journals Increasing Network Lifetime In Cluster Based Wireless Sensor Networks Via Fuzzy Logic

2021 ◽  
Author(s):  
Rouzbeh Behrouz
Keyword(s):  
Wireless Sensor Networks ◽  
Fuzzy Logic ◽  
Sensor Networks ◽  
Large Scale ◽  
Fuzzy Inference ◽  
Critical Issue ◽  
Base Station ◽  
Wireless Sensor ◽  
Inference System ◽  
Efficient Operation

Energy efficient operation is a critical issue that has to be addressed with large-scale wireless sensor networks deployments. Cluster-based protocols are developed to tackle this problem and Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the best-known protocols of this type. However, certain aspects of LEACH offer room for improvement. One such aspect is the arrangement of wireless sensor network with the fixed base station location. In this thesis we purpose Fuzzy Logic for Mobile Base Station (FLMBS) protocol that is based on LEACH but uses a Fuzzy Inference System driven approach to adjust the location of the base station. FLMBS produces reasonable improvement over LEACH in a network area greater than 1000 x 1000 m

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