Artificial Intelligence-Based Model For Drought Prediction and Forecasting

2019 ◽  
Vol 63 (11) ◽  
pp. 1704-1712 ◽  
Author(s):  
Amandeep Kaur ◽  
Sandeep K Sood
Keyword(s):  
Singular Vector ◽  
Wildlife Habitat ◽  
Drought Indices ◽  
Drought Severity ◽  
Efficient System ◽  
Drought Prediction ◽  
Vector Decomposition ◽  
Prediction And Forecasting ◽  
The Given ◽  
Catastrophic Impact

Abstract Drought is considered as one of the most extremely destructive natural disasters with catastrophic impact on hydrological balance, agriculture outcome, wildlife habitat and financial budget. Therefore, there is a need for an efficient system to predict and forecast drought situations. There are a number of drought indices to assess the severity of droughts considering different causing factors. Most of them does not take important factors into consideration. Internet of Things (IoT) has demonstrated phenomenal growth and has successfully worked in monitoring environmental conditions. This paper proposes an IoT-enabled fog-based framework for the prediction and forecasting of droughts. At the fog layer, the dimensions of the data are decreased using singular vector decomposition. Artificial neural network with genetic algorithm classifier is used to assess drought severity category to the given event and Holt-Winters method is used to predict the future drought conditions. The proposed system is implemented using datasets from government agencies and it proves its effectiveness in assessing drought severity level.

scholarly journals Comprehensive Drought Analysis Using Statistical and Meteorological Indices Approach: A Case Study of Badin, Sindh

2019 ◽  
pp. 594-604
Author(s):  
Aisha Akber ◽  
Syed Feroz Shah ◽  
Muhammad Wajid Ijaz ◽  
Hira Soomro ◽  
Nimra Alam ◽  
...  
Keyword(s):  
Arid Zone ◽  
Standardized Precipitation Index ◽  
Drought Indices ◽  
Drought Severity ◽  
Climate Projections ◽  
Arid Zones ◽  
Data Set ◽  
Drought Analysis ◽  
Drought Prediction ◽  
Average Annual Temperature

Drought is a global phenomenon that can occur in any ecological zone and render significant damages to both the natural environment and human lives. However, hydro-climatic stresses are growing distinctly in the arid zones across the globe. Literature suggests that the analysis of a long-term data-set could help in strengthening of mitigation planes and rationalization of disaster management policies. Thus, the present study is aimed to analyze the evidence-based historical drought events happened in arid-zone Badin, Pakistan and predict its occurrence and severity for the next 82 years (2018-2099). Drought indices viz standardized precipitation index and reconnaissance drought index have been used to detect the severity of the drought events. Thirty years (1988 to 2017) past data of precipitation and temperature were used to categorize the drought severity and validated against the local data. Climate projections based on RCP 4.5 and 8.5 made at 25x25 km resolution used for future drought analysis. The results demonstrate that the region faced severe to extreme drought in 1990-91 and 2001-04. While, in future 2020-21, 2036-37, 2038-39 would be the extreme driest years under RCP 4.5 and 2029-30, 2089-90 under RCP 8.5. Further insight revealed that the average annual temperature has increased and precipitation has decreased w.r.t the base year 1988. It is concluded that drought detection with SPI and RDI is suitable and drought prediction with the RCP 4.5 and 8.5 could be a better option.

scholarly journals Retrieving Sun-Induced Chlorophyll Fluorescence from Hyperspectral Data with TanSat Satellite

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4886
Author(s):  
Shilei Li ◽  
Maofang Gao ◽  
Zhao-Liang Li
Keyword(s):  
Chlorophyll Fluorescence ◽  
Fluorescence Spectrum ◽  
Singular Vector ◽  
Low Frequency ◽  
Information Criterion ◽  
Hyperspectral Data ◽  
High Spectral Resolution ◽  
Remotely Sensed Data ◽  
Spectral Window ◽  
Vector Decomposition

A series of algorithms for satellite retrievals of sun-induced chlorophyll fluorescence (SIF) have been developed and applied to different sensors. However, research on SIF retrieval using hyperspectral data is performed in narrow spectral windows, assuming that SIF remains constant. In this paper, based on the singular vector decomposition (SVD) technique, we present an approach for retrieving SIF, which can be applied to remotely sensed data with ultra-high spectral resolution and in a broad spectral window without assuming that the SIF remains constant. The idea is to combine the first singular vector, the pivotal information of the non-fluorescence spectrum, with the low-frequency contribution of the atmosphere, plus a linear combination of the remaining singular vectors to express the non-fluorescence spectrum. Subject to instrument settings, the retrieval was performed within a spectral window of approximately 7 nm that contained only Fraunhofer lines. In our retrieval, hyperspectral data of the O2-A band from the first Chinese carbon dioxide observation satellite (TanSat) was used. The Bayesian Information Criterion (BIC) was introduced to self-adaptively determine the number of free parameters and reduce retrieval noise. SIF retrievals were compared with TanSat SIF and OCO-2 SIF. The results showed good consistency and rationality. A sensitivity analysis was also conducted to verify the performance of this approach. To summarize, the approach would provide more possibilities for retrieving SIF from hyperspectral data.

Projection of Future Drought Characteristics under Multiple Drought Indices

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1238
Author(s):  
Muhammad Imran Khan ◽  
Xingye Zhu ◽  
Xiaoping Jiang ◽  
Qaisar Saddique ◽  
Muhammad Saifullah ◽  
...  
Keyword(s):  
Series Data ◽  
Natural Phenomenon ◽  
Drought Indices ◽  
Drought Severity ◽  
Severe Drought ◽  
Drought Analysis ◽  
Songhua River ◽  
Drought Intensity ◽  
Drought Characteristics ◽  
Rcp 8.5

Drought is a natural phenomenon caused by the variability of climate. This study was conducted in the Songhua River Basin of China. The drought events were estimated by using the Reconnaissance Drought Index (RDI) and Standardized Precipitation Index (SPI) which are based on precipitation (P) and potential evapotranspiration (PET) data. Furthermore, drought characteristics were identified for the assessment of drought trends in the study area. Short term (3 months) and long term (12 months) projected meteorological droughts were identified by using these drought indices. Future climate precipitation and temperature time series data (2021–2099) of various Representative Concentration Pathways (RCPs) were estimated by using outputs of the Global Circulation Model downscaled with a statistical methodology. The results showed that RCP 4.5 have a greater number of moderate drought events as compared to RCP 2.6 and RCP 8.5. Moreover, it was also noted that RCP 8.5 (40 events) and RCP 4.5 (38 events) showed a higher number of severe droughts on 12-month drought analysis in the study area. A severe drought conditions projected between 2073 and 2076 with drought severity (DS-1.66) and drought intensity (DI-0.42) while extreme drying trends were projected between 2097 and 2099 with drought severity (DS-1.85) and drought intensity (DI-0.62). It was also observed that Precipitation Decile predicted a greater number of years under deficit conditions under RCP 2.6. Overall results revealed that more severe droughts are expected to occur during the late phase (2050–2099) by using RDI and SPI. A comparative analysis of 3- and 12-month drying trends showed that RDI is prevailing during the 12-month drought analysis while almost both drought indices (RDI and SPI) indicated same behavior of drought identification at 3-month drought analysis between 2021 and 2099 in the research area. The results of study will help to evaluate the risk of future drought in the study area and be beneficial for the researcher to make an appropriate mitigation strategy.

scholarly journals Assessment of the Dissimilarities of EDI and SPI Measures for Drought Determination in South Africa

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 82
Author(s):  
Omolola M. Adisa ◽  
Muthoni Masinde ◽  
Joel O. Botai
Keyword(s):  
South Africa ◽  
South African ◽  
Correlation Coefficient ◽  
Pearson Correlation ◽  
Standardized Precipitation Index ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Drought Severity ◽  
Pearson Correlation Coefficient ◽  
Drought Duration

This study examines the (dis)similarity of two commonly used indices Standardized Precipitation Index (SPI) computed over accumulation periods 1-month, 3-month, 6-month, and 12-month (hereafter SPI-1, SPI-3, SPI-6, and SPI-12, respectively) and Effective Drought Index (EDI). The analysis is based on two drought monitoring indicators (derived from SPI and EDI), namely, the Drought Duration (DD) and Drought Severity (DS) across the 93 South African Weather Service’s delineated rainfall districts over South Africa from 1980 to 2019. In the study, the Pearson correlation coefficient dissimilarity and periodogram dissimilarity estimates were used. The results indicate a positive correlation for the Pearson correlation coefficient dissimilarity and a positive value for periodogram of dissimilarity in both the DD and DS. With the Pearson correlation coefficient dissimilarity, the study demonstrates that the values of the SPI-1/EDI pair and the SPI-3/EDI pair exhibit the highest similar values for DD, while the SPI-6/EDI pair shows the highest similar values for DS. Moreover, dissimilarities are more obvious in SPI-12/EDI pair for DD and DS. When a periodogram of dissimilarity is used, the values of the SPI-1/EDI pair and SPI-6/EDI pair exhibit the highest similar values for DD, while SPI-1/EDI displayed the highest similar values for DS. Overall, the two measures show that the highest similarity is obtained in the SPI-1/EDI pair for DS. The results obtainable in this study contribute towards an in-depth knowledge of deviation between the EDI and SPI values for South Africa, depicting that these two drought indices values are replaceable in some rainfall districts of South Africa for drought monitoring and prediction, and this is a step towards the selection of the appropriate drought indices.

scholarly journals Seasonal drought prediction for semiarid northeastern Brazil: verification of six hydro-meteorological forecast products

2018 ◽  
Vol 22 (9) ◽  
pp. 5041-5056 ◽  
Author(s):  
José Miguel Delgado ◽  
Sebastian Voss ◽  
Gerd Bürger ◽  
Klaus Vormoor ◽  
Aline Murawski ◽  
...  
Keyword(s):  
Rainy Season ◽  
Meteorological Drought ◽  
Skill Score ◽  
Hydrological Drought ◽  
Northeastern Brazil ◽  
Drought Indices ◽  
Monthly Precipitation ◽  
Seasonal Forecasts ◽  
Seasonal Drought ◽  
Drought Prediction

Abstract. A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará's research foundation for meteorology) and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation, (b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil.

scholarly journals Effects of different reference periods on drought index (SPEI) estimations from 1901 to 2014

2017 ◽  
Vol 21 (10) ◽  
pp. 4989-5007 ◽  
Author(s):  
Myoung-Jin Um ◽  
Yeonjoo Kim ◽  
Daeryong Park ◽  
Jeongbin Kim
Keyword(s):  
West Africa ◽  
East Asia ◽  
The United States ◽  
Spatial Extent ◽  
Drought Indices ◽  
Drought Severity ◽  
Climate Change Scenarios ◽  
Reference Period ◽  
Drought Characteristics ◽  
Regional Drought

Abstract. This study aims to understand how different reference periods (i.e., calibration periods) of climate data used to estimate drought indices influence regional drought assessments. Specifically, we investigate the influences of different reference periods on historical drought characteristics, such as the trend, frequency, intensity and spatial extent, using the standardized precipitation evapotranspiration index (SPEI) with a 12-month lag (SPEI-12), which was estimated from the datasets of the Climate Research Unit (CRU) and the University of Delaware (UDEL). For the 1901–1957 (P1) and 1958–2014 (P2) estimation periods, three different types of reference periods are used to compute the SPEI: P1 and P2 together, P1 and P2 separately and P1 only. Focusing on East Asia, Europe, the United States and West Africa, we find that the influence of the reference period is significant in East Asia and West Africa, with dominant drying trends from P1 to P2. The reference period influenced the assessment of drought characteristics, particularly the severity and spatial extent, whereas the influence on the frequency was relatively small. Finally, self-calibration, which is the most common practice for indices such as the SPEI, tends to underestimate the drought severity and spatial extent relative to the other approaches used in this study. Although the conclusions drawn in this study are limited by the use of two global datasets, they highlight the need for clarification of the reference period in drought assessments to better understand regional drought characteristics and the associated temporal changes, particularly under climate change scenarios.

scholarly journals Optimized Multi-layer Machine Learning Technique for Movement Detection and Tracking in Video Data Stream

2019 ◽  
Vol 8 (10) ◽  
pp. 1151-1159
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Error Rates ◽  
Moving Object Detection ◽  
Video Data ◽  
Moving Object ◽  
Detection Accuracy ◽  
Efficient System ◽  
Detection And Tracking ◽  
The Given

With the advent in technology, security and authentication has become the main aspect in computer vision approach. Moving object detection is an efficient system with the goal of preserving the perceptible and principal source in a group. Surveillance is one of the most crucial requirements and carried out to monitor various kinds of activities. The detection and tracking of moving objects are the fundamental concept that comes under the surveillance systems. Moving object recognition is challenging approach in the field of digital image processing. Moving object detection relies on few of the applications which are Human Machine Interaction (HMI), Safety and video Surveillance, Augmented Realism, Transportation Monitoring on Roads, Medical Imaging etc. The main goal of this research is the detection and tracking moving object. In proposed approach, based on the pre-processing method in which there is extraction of the frames with reduction of dimension. It applies the morphological methods to clean the foreground image in the moving objects and texture based feature extract using component analysis method. After that, design a novel method which is optimized multilayer perceptron neural network. It used the optimized layers based on the Pbest and Gbest particle position in the objects. It finds the fitness values which is binary values (x_update, y_update) of swarm or object positions. Method and output achieved final frame creation of the moving objects in the video using BLOB ANALYSER In this research , an application is designed using MATLAB VERSION 2016a In activation function to re-filter the given input and final output calculated with the help of pre-defined sigmoid. In proposed methods to find the clear detection and tracking in the given dataset MOT, FOOTBALL, INDOOR and OUTDOOR datasets. To improve the detection accuracy rate, recall rate and reduce the error rates, False Positive and Negative rate and compare with the various classifiers such as KNN, MLPNN and J48 decision Tree.

scholarly journals Analysis of drought characteristics for improved understanding of a water resource system

2014 ◽  
Vol 364 ◽  
pp. 404-409 ◽  
Author(s):  
A. T. Lennard ◽  
N. Macdonald ◽  
J. Hooke
Keyword(s):  
Resource Management ◽  
Water Supply ◽  
Water Resource ◽  
Water Resource Management ◽  
Drought Indices ◽  
Drought Severity ◽  
Small Scale ◽  
Water Resource System ◽  
Drought Characteristics ◽  
Standardised Precipitation Index

Abstract. Droughts are a reoccurring feature of the UK climate; recent drought events (2004–2006 and 2010–2012) have highlighted the UK’s continued vulnerability to this hazard. There is a need for further understanding of extreme events, particularly from a water resource perspective. A number of drought indices are available, which can help to improve our understanding of drought characteristics such as frequency, severity and duration. However, at present little of this is applied to water resource management in the water supply sector. Improved understanding of drought characteristics using indices can inform water resource management plans and enhance future drought resilience. This study applies the standardised precipitation index (SPI) to a series of rainfall records (1962–2012) across the water supply region of a single utility provider. Key droughts within this period are analysed to develop an understanding of the meteorological characteristics that lead to, exist during and terminate drought events. The results of this analysis highlight how drought severity and duration can vary across a small-scale water supply region, indicating that the spatial coherence of drought events cannot be assumed.

A novel index for ecological drought monitoring based on ecological water deficit: a case study of Northwestern China

2021 ◽  
Author(s):  
Tianliang Jiang ◽  
Xiaoling Su
Keyword(s):  
Water Deficit ◽  
Time Scales ◽  
Drought Index ◽  
Vegetation Index ◽  
Standardized Precipitation Index ◽  
Northwestern China ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Drought Severity ◽  
Different Time Scales

<p>Although the concept of ecological drought was first defined by the Science for Nature and People Partnership (SNAPP) in 2016, there remains no widely accepted drought index for monitoring ecological drought. Therefore, this study constructed a new ecological drought monitoring index, the standardized ecological water deficit index (SEWDI). The SEWDI is based on the difference between ecological water requirements and consumption, referred to as the standardized precipitation index (SPI) method, which was used to monitor ecological drought in Northwestern China (NWRC). The performances of the SEWDI and four widely-used drought indices [standardized root soil moisture index (SSI), self-calibrated Palmer drought index (scPDSI), standardized precipitation-evaporation drought index (SPEI), and SPI) in monitoring ecological drought were evaluated through comparing the Pearson correlations between these indices and the standardized normalized difference vegetation index (SNDVI) under different time scales, wetness, and water use efficiencies (WUEs) of vegetation. Finally, the rotational empirical orthogonal function (REOF) was used to decompose the SEWDI at a 12-month scale in the NWRC during 1982–2015 to obtain five ecological drought regions. The characteristics of ecological drought in the NWRC, including intensity, duration, and frequency, were extracted using run theory. The results showed that the performance of the SEWDI in monitoring ecological drought was highest among the commonly-used drought indices evaluated under different time scales [average correlation coefficient values (r) between SNDVI and drought indices: SEWDI<sub></sub>= 0.34, SSI<sub></sub>= 0.24, scPDSI<sub></sub>= 0.23, SPI<sub></sub>= 0.20, SPEI<sub></sub>= 0.18), and the 12-month-scale SEWDI was largely unaffected by wetness and WUE. In addition, the results of the monitoring indicated that serious ecological droughts in the NWRC mainly occurred in 1982–1986, 1990–1996, and 2005–2010, primarily in regions I, II, and V, regions II, and IV, and in region III, IV, and V, respectively. This study provides a robust approach for quantifying ecological drought severity across natural vegetation areas and scientific evidence for governmental decision makers.</p>

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