Simulation of leaf blast infection in tropical rice agro-ecology under climate change scenario     

K. Viswanath  P. Sinha  S. Naresh Kumar  Taru Sharma  Shalini Saxena  Shweta Panjwani  H. Pathak  Shalu Mishra Shukla 《Climatic change》2017,142(1-2):155-167

Assessing disease risk has become an important component in the development of climate change adaptation strategies. Here, the infection ability of leaf blast (Magnaporthe oryzae) was modeled based on the epidemiological parameters of minimum (T _min), optimum (T _opt), and maximum (T _max) temperatures for sporulation and lesion development. An infection ability response curve was used to assess the impact of rising temperature on the disease. The simulated spatial pattern of the infection ability index (IAI) corresponded with observed leaf blast occurrence in Indo-Gangetic plains (IGP). The IAI for leaf blast is projected to increase during the winter season (December–March) in 2020 (2010–2039) and 2050 (2040–2069) climate scenarios due to temperature rise, particularly in lower latitudes. However, during monsoon season (July–October), the IAI is projected to remain unchanged or even reduce across the IGP. The results show that the response curve may be successfully used to assess the impact of climate change on leaf blast in rice. The model could be further extended with a crop model to assess yield loss.  相似文献   

Neural networks for wave forecasting   总被引：1，自引：0，他引：1  

M. C. Deo  A. Jha  A. S. Chaphekar  K. Ravikant 《Ocean Engineering》2001,28(7)

The physical process of generation of waves by wind is extremely complex, uncertain and not yet fully understood. Despite a variety of deterministic models presented to predict the heights and periods of waves from the characteristics of the generating wind, a large scope still exists to improve on the existing models or to provide alternatives to them. This paper explores the possibility of employing the relatively recent technique of neural networks for this purpose. A simple 3-layered feed forward type of network is developed to obtain the output of significant wave heights and average wave periods from the input of generating wind speeds. The network is trained with different algorithms and using three sets of data. The results show that an appropriately trained network could provide satisfactory results in open wider areas, in deep water and also when the sampling and prediction interval is large, such as a week. A proper choice of training patterns is found to be crucial in achieving adequate training.  相似文献   

Characteristics of VHF scintillations in the equatorial anomaly crest region in India     

K. N. Pathak  R. D. Jivrajani  H. P. Joshi  K. N. Iyer 《Annales Geophysicae》1995,13(7):730-739

The characteristics of ionospheric scintillations at Rajkot in the equatorial anomaly crest region in India are described for the years 1987–1991 by monitoring the 244-MHz transmission from the satellite FLEETSAT. This period covers the ascending phase of solar cycle 22. Scintillations occur predominantly in the pre-midnight period during equinoxes and winter seasons and in the post-midnight period during summer season. During equinoxes and winter, scintillation occurrence increases with solar activity, whilst in summer it is found to decrease with solar activity. Statistically, scintillation occurrence is suppressed by magnetic activity. The characteristics observed during winter and equinoxes are similar to those seen at the equatorial station, Trivandrum. This, coupled with the nature of the post-sunset equatorial F-region drift and h‘F variations, supports the view that at the anomaly crest station, scintillations are of equatorial origin during equinox and winter, whilst in summer they may be of mid-latitude type. The variations in scintillation intensity (in dB) with season and solar activity are also reported.  相似文献   

Characteristics of landslide in Koshi River Basin,Central Himalaya   总被引：1，自引：0，他引：1  

Jian-qiang?ZhangEmail author  Rong-kun?Liu  Wei?Deng  Narendra?Raj?Khanal  Deo?Raj?Gurung  Manchiraju?Sri?Ramachandra?Murthy  Shahriar?Wahid 《山地科学学报》2016,13(10):1711-1722

Koshi River basin, which lies in the Central Himalayas with an area of 71,500 km2, is an important trans-boundary river basin shared by China, Nepal and India. Yet, landslide-prone areas are all located in China and Nepal, imposing alarming risks of widespread damages to property and loss of human life in both countries. Against this backdrop, this research, by utilizing remote sensing images and topographic maps, has identified a total number of 6877 landslides for the past 23 years and further examined their distribution, characteristics and causes. Analysis shows that the two-step topography in the Himalayan region has a considerable effect on the distribution of landslides in this area. Dense distribution of landslides falls into two regions: the Lesser Himalaya(mostly small and medium size landslides in east-west direction) and the TransitionBelt(mostly large and medium size landslides along the river in north-south direction). Landslides decrease against the elevation while the southern slopes of the Himalayas have more landslides than its northern side. Change analysis was carried out by comparing landslide distribution data of 1992, 2010 and 2015 in the Koshi River basin. The rainfallinduced landslides, usually small and shallow and occurring more frequently in regions with an elevation lower than 1000 m, are common in the south and south-east slopes due to heavy precipitation in the region, and are more prone to the slope gradient of 20°~30°. Most of them are distributed in Proterozoic stratum(Pt3ε, Pt3 and Pt2-3) and Quaternary stratum. While for earthquake-induced landslides, they are more prone to higher elevations(2000~3000 m) and steeper slopes(40°~50°).  相似文献   

Quantitative definition and spatiotemporal distribution of little water season (LIWAS) in Korea     

Su-Jeong Kim  Hi-Ryong Byun  Ravinesh C. Deo 《Asia-Pacific Journal of Atmospheric Sciences》2016,52(4):379-393

Like other continental climatic regions Korea has a period around the spring when agricultural activities are interrupted frequently by a shortage of available water resources during the season. This season, which is termed the Little Water Season (LIWAS) in this study, has important implications for many socio-economic activities but the scientific definition of this season remains vague. In this study, the onset and termination dates, as well as the characteristics of the LIWAS have been defined based on the Available Water Resources Index (AWRI). Based on the proposed definition of LIWAS, the implications on hydrological conditions over a range of geographic scales and their inter-annual variations on the water resource environments in Korea have been assessed. To develop an appropriate index for LIWAS based on AWRI, the criterion value (CV) for LIWAS was set as the lowest 25th percentile of the AWRI values averaged for 30 years (1981-2010). Therefore, the Little Water Season for Korea (LIWAS_K) was considered as the period when the daily averaged AWRIs were successively lower than the CV (143.7 mm). Based on this, the mean onset and end date of LIWAS_K, was 9 February and 11 May which also reflected the period in the spring season when the available water resources are expected to the lowest. Moreover, a number of seasonal characteristics of the water availability during the LIWAS, such as the Little Water Intensity (LWI), Water Deficit Amount (WDA) and Water Deficit Intensity (WDI) have been defined for the particular study region. Based on our results, we aver that the proposed season classification of the LIWAS can be better analyzed using the concept of usable water resources as a classification of dry period instead of using temperature and raw rainfall datasets.  相似文献   

The possibility of the Oceanic Anoxic Events (OAEs) study in the Indian marine Jurassic - Cretaceous outcrops     

Bindhyachal Pandey  Deo Brat Pathak 《Journal of the Geological Society of India》2016,87(3):261-267

  相似文献   

Estimation of monthly evaporative loss using relevance vector machine,extreme learning machine and multivariate adaptive regression spline models   总被引：1，自引：0，他引：1  

Ravinesh C. Deo  Pijush Samui  Dookie Kim 《Stochastic Environmental Research and Risk Assessment (SERRA)》2016,30(6):1769-1784

The forecasting of evaporative loss (E) is vital for water resource management and understanding of hydrological process for farming practices, ecosystem management and hydrologic engineering. This study has developed three machine learning algorithms, namely the relevance vector machine (RVM), extreme learning machine (ELM) and multivariate adaptive regression spline (MARS) for the prediction of E using five predictor variables, incident solar radiation (S), maximum temperature (T _max), minimum temperature (T _min), atmospheric vapor pressure (VP) and precipitation (P). The RVM model is based on the Bayesian formulation of a linear model with appropriate prior that results in sparse representations. The ELM model is computationally efficient algorithm based on Single Layer Feedforward Neural Network with hidden neurons that randomly choose input weights and the MARS model is built on flexible regression algorithm that generally divides solution space into intervals of predictor variables and fits splines (basis functions) to each interval. By utilizing random sampling process, the predictor data were partitioned into the training phase (70 % of data) and testing phase (remainder 30 %). The equations for the prediction of monthly E were formulated. The RVM model was devised using the radial basis function, while the ELM model comprised of 5 inputs and 10 hidden neurons and used the radial basis activation function, and the MARS model utilized 15 basis functions. The decomposition of variance among the predictor dataset of the MARS model yielded the largest magnitude of the Generalized Cross Validation statistic (≈0.03) when the T _max was used as an input, followed by the relatively lower value (≈0.028, 0.019) for inputs defined by the S and VP. This confirmed that the prediction of E utilized the largest contributions of the predictive features from the T _max, verified emphatically by sensitivity analysis test. The model performance statistics yielded correlation coefficients of 0.979 (RVM), 0.977 (ELM) and 0.974 (MARS), Root-Mean-Square-Errors of 9.306, 9.714 and 10.457 and Mean-Absolute-Error of 0.034, 0.035 and 0.038. Despite the small differences in the overall prediction skill, the RVM model appeared to be more accurate in prediction of E. It is therefore advocated that the RVM model can be employed as a promising machine learning tool for the prediction of evaporative loss.  相似文献   

Elevational distribution of butterflies in the Himalayas: a case study from Langtang National Park,Nepal     

Ranjit Pandey  Kapil K. Khadka  Anuj Ghimire  Puja Jha  Umesh Pathak 《山地科学学报》2017,14(7):1384-1390

Mountain ecosystems are relatively more vulnerable to climate change since human induced climate change is projected to be higher at high altitudes and latitudes. Climate change induced effects related to glacial response and water hazards have been documented in the Himalayas in recent years, yet studies regarding species’ response to climate change are largely lacking from the mountains and Himalayas of Nepal. Changes in distribution and latitudinal/altitudinal range shift, which are primary adaptive responses to climate change in many species, are largely unknown due to unavailability of adequate data from the past. In this study, we explored the elevational distribution of butterflies in Langtang Village Development Committee (VDC) of Langtang National park; a park located in the high altitudes of Nepal. We found a decreasing species richness pattern along the elevational gradient considered here. Interestingly, elevation did not appear to have a significant effect on the altitudinal distribution of butterflies at family level. Also, distribution of butterflies in the area was independent of habitat type, at family level. Besides, we employed indicator group analysis (at family level) and noticed that butterfly families Papilionidae, Riodinidae, and Nymphalidae are significantly associated to high, medium and low elevational zone making them indicator butterfly family for those elevational zones, respectively. We expect that this study could serve as a baseline information for future studies regarding climate change effects and range shifts and provide avenues for further exploration of butterflies in the high altitudes of Nepal.  相似文献   

Estimation of wave directional spreading in shallow water     

V. Sanil Kumar  M. C. Deo  N. M. Anand  P. Chandramohan 《Ocean Engineering》1999,26(1):83

This paper describes wave directional spreading in shallow water. Waves were measured for a period of 2 months using the Datawell directional waverider buoy at 15 m water depth on the east coast of India in the Bay of Bengal. The study also showed that in shallow water wave directional spreading was narrowest at peak frequency and widened towards lower and higher frequencies. The wind direction was found to deviate from the wave direction during most of the time. The unidirectional spectrum was found to be satisfactorily represented by Scott spectra.  相似文献   

Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir,Turkey     

Mohammad?Ali?GhorbaniEmail author  Ravinesh?C.?Deo  Vahid?Karimi  Zaher?Mundher?Yaseen  Ozlem?Terzi 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(6):1683-1697

The predictive ability of a hybrid model integrating the Firefly Algorithm (FFA), as a heuristic optimization tool with the Multilayer Perceptron (MLP-FFA) algorithm for the prediction of water level in Lake Egirdir, Turkey, is investigated. The accuracy of the hybrid MLP-FFA model is then evaluated against the standalone MLP-based model developed with the Levenberg–Marquadt optimization scheme applied for in the backpropagation-based learning process. To develop and investigate the veracity of the proposed hybrid MLP-FFA model, monthly time scale water level data for 56 years (1961–2016) are applied to train and test the hybrid model. The input combinations of the standalone and the hybrid predictive models are determined in accordance with the Average Mutual Information computed from the historical water level (training) data; generating four statistically significant lagged combinations of historical data to be adopted for the 1-month forecasting of lake water level. The proposed hybrid MLP-FFA model is evaluated with statistical score metrics: Nash–Sutcliffe efficiency, root mean square and mean absolute error, Wilmott’s Index and Taylor diagram developed in the testing phase. The analysis of the results showed that the hybrid MLP–FFA4 model (where 4 months of lagged combinations of lake water level data are utilized) performed more accurately than the standalone MLP4 model. For the fully optimized hybrid (MLP-FFA4) model evaluated in the testing phase, the Willmott’s Index was approximately 0.999 relative to 0.988 (MLP 4) and the root mean square error was approximately 0.029 m and compared to 0.102 m. Moreover, the inter-comparison of the forecasted and the observed data with various other performance metrics (including the Taylor diagram) verified the robustness of the proposed hybrid MLP-FFA4 model over the standalone MLP4 model applied in the problem of forecasting lake water level prediction in the current semi-arid region in Turkey.  相似文献